From 5efb877df7316bcdecba2388bc5d81d3ad57eab6 Mon Sep 17 00:00:00 2001 From: hc Date: Sat, 28 Feb 2026 16:17:42 +0800 Subject: [PATCH] Initial commit: add project materials and code --- .gitattributes | 3 + .gitignore | 24 + ...de Selection for Semantic Communication.pdf | 3 + ... 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create mode 100644 paper/08_conclusion.md create mode 100644 paper/09_references.md create mode 100644 paper/main.md create mode 100644 paper/notation.md create mode 100644 paper1.txt create mode 100644 paper2.txt create mode 100644 paper3.txt diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..d7efecd --- /dev/null +++ b/.gitattributes @@ -0,0 +1,3 @@ +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.pdf filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..270cac2 --- /dev/null +++ b/.gitignore @@ -0,0 +1,24 @@ +# Python +__pycache__/ +*.py[cod] +*.pyd +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ + +# Virtual environments +.venv/ +venv/ +ENV/ +env/ + +# Editors/IDEs +.vscode/ +.idea/ + +# OS files +.DS_Store +Thumbs.db + +# Logs +*.log diff --git a/Noh 等 - Deep Reinforcement Learning-Based Resource Allocation and Mode Selection for Semantic Communication.pdf b/Noh 等 - Deep Reinforcement Learning-Based Resource Allocation and Mode Selection for Semantic Communication.pdf new file mode 100644 index 0000000..cb9c753 --- /dev/null +++ b/Noh 等 - Deep Reinforcement Learning-Based Resource Allocation and Mode Selection for Semantic Communication.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:17e5504c679f03b39fe62227230777d834fe671c7a61fe01810d9459a9a3c3d7 +size 570118 diff --git a/Xie 等 - 2025 - Hybrid Digital-Analog Semantic Communications.pdf b/Xie 等 - 2025 - Hybrid Digital-Analog Semantic Communications.pdf new file mode 100644 index 0000000..9365b4f --- /dev/null +++ b/Xie 等 - 2025 - Hybrid Digital-Analog Semantic Communications.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9cff56a194f77475cebb135905d0d6085003eb3920bcee397189f11c74749cd2 +size 3024507 diff --git a/Zhang 等 - 2026 - Resource Allocation in Wireless Semantic Communications A Comprehensive Survey.pdf b/Zhang 等 - 2026 - Resource Allocation in Wireless Semantic Communications A Comprehensive Survey.pdf new file mode 100644 index 0000000..8af4089 --- /dev/null +++ b/Zhang 等 - 2026 - Resource Allocation in Wireless Semantic Communications A Comprehensive Survey.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12ba42a87e6b233693706e6340f6f33060c70c0affcf7471c26a43f52f29ea0c +size 7384079 diff --git a/code/API.md b/code/API.md new file mode 100644 index 0000000..f1f3a2d --- /dev/null +++ b/code/API.md @@ -0,0 +1,599 @@ +# API 接口文档 / API Reference + +本文档详细描述了 Co-MADDPG 项目中所有公开类和函数的接口。 + +--- + +## 目录 / Table of Contents + +1. [环境模块 envs/](#1-环境模块-envs) + - [ChannelModel](#channelmodel) + - [SemanticModule](#semanticmodule) + - [WirelessEnv](#wirelessenv) +2. [算法模块 agents/](#2-算法模块-agents) + - [Actor](#actor) + - [Critic](#critic) + - [OUNoise](#ounoise) + - [ReplayBuffer](#replaybuffer) + - [CoMADDPG](#comaddpg) +3. [基线模块 baselines/](#3-基线模块-baselines) + - [通用接口](#通用接口--common-interface) + - [各基线差异](#各基线差异--baseline-differences) +4. [工具模块 utils/](#4-工具模块-utils) + - [metrics.py](#metricspy) + - [visualization.py](#visualizationpy) +5. [入口脚本](#5-入口脚本--entry-scripts) + - [train.py](#trainpy) + - [evaluate.py](#evaluatepy) + +--- + +## 1. 环境模块 envs/ + +### ChannelModel + +**文件**: `envs/channel_model.py` + +3GPP Urban Micro NLOS 信道模型，负责路径损耗计算、复信道增益生成和 SNR 计算。 + +```python +class ChannelModel: + def __init__(self, config: dict) -> None +``` + +| 参数 | 类型 | 说明 | +|---|---|---| +| `config` | dict | 完整配置字典，需包含 `config["env"]["carrier_freq"]`, `config["env"]["noise_psd"]`, `config["env"]["subcarrier_spacing"]` | + +#### 方法 + +**`path_loss(distance) -> float`** + +计算 3GPP UMi NLOS 路径损耗。 + +| 参数 | 类型 | 说明 | +|---|---|---| +| `distance` | float / np.ndarray | 收发机距离 (米) | +| **返回** | float / np.ndarray | 路径损耗 (dB) | + +公式: `PL(d) = 36.7·log₁₀(d) + 22.7 + 26·log₁₀(fc)` + +--- + +**`generate_channel(distances, num_subcarriers) -> np.ndarray`** + +生成复信道增益矩阵。 + +| 参数 | 类型 | 说明 | +|---|---|---| +| `distances` | np.ndarray (K,) | 每个用户的距离 | +| `num_subcarriers` | int | 子载波数 N | +| **返回** | np.ndarray (K, N) | 复信道增益 `h_{k,n} ~ CN(0, 10^{-PL/10})` | + +--- + +**`compute_snr(channel_gains, power_alloc, noise_power) -> np.ndarray`** + +计算每用户每子载波的 SNR。 + +| 参数 | 类型 | 说明 | +|---|---|---| +| `channel_gains` | np.ndarray (K, N) | 复信道增益 | +| `power_alloc` | np.ndarray (K, N) | 功率分配矩阵 (W) | +| `noise_power` | float | 每子载波噪声功率 σ² (W) | +| **返回** | np.ndarray (K, N) | SNR (线性尺度) | + +公式: `γ_{k,n} = p_{k,n} · |h_{k,n}|² / σ²` + +--- + +**`noise_power` (property) -> float** + +每子载波热噪声功率 (W)。 + +公式: `σ² = 10^{(N₀_dBm - 30)/10} · Δf` + +--- + +### SemanticModule + +**文件**: `envs/semantic_module.py` + +语义通信质量模块，计算 SSim 和语义 QoE。 + +```python +class SemanticModule: + def __init__(self, config: dict) -> None +``` + +| 参数 | 类型 | 说明 | +|---|---|---| +| `config` | dict | 需包含 `config["env"]["rho_max"]`, `rho_min`, `w1`, `w2` | + +#### 方法 + +**`compute_ssim(avg_snr, rho) -> float`** + +计算语义相似度指数。 + +| 参数 | 类型 | 说明 | +|---|---|---| +| `avg_snr` | float / np.ndarray | 平均 SNR (线性尺度) | +| `rho` | float | 压缩率 ρ ∈ [ρ_min, ρ_max] | +| **返回** | float / np.ndarray | SSim ∈ [0, 1] | + +公式: `φ(γ̄, ρ) = 1 - exp(-a(ρ)·γ̄^{b(ρ)})`，其中 `a(ρ) = 0.8/(ρ+0.1)`, `b(ρ) = 0.6+0.2·ρ` + +--- + +**`compute_avg_snr(snr_per_subcarrier, allocation_mask) -> float`** + +计算已分配子载波的平均 SNR。 + +| 参数 | 类型 | 说明 | +|---|---|---| +| `snr_per_subcarrier` | np.ndarray | 所有子载波的 SNR | +| `allocation_mask` | np.ndarray | 二进制掩码 (1=已分配) | +| **返回** | float | 平均 SNR (无分配时返回 0.0) | + +--- + +**`compute_semantic_qoe(ssim, rho, w1=None, w2=None, rho_max=None) -> float`** + +计算语义用户 QoE。 + +| 参数 | 类型 | 说明 | +|---|---|---| +| `ssim` | float | 语义相似度 ∈ [0, 1] | +| `rho` | float | 压缩率 | +| `w1`, `w2` | float, optional | 权重 (默认使用配置值) | +| `rho_max` | float, optional | 最大压缩率 (默认使用配置值) | +| **返回** | float | QoE ∈ [0, 1] | + +公式: `QoE_s = w1·SSim + w2·(1 - ρ/ρ_max)` + +--- + +### WirelessEnv + +**文件**: `envs/wireless_env.py` + +Gym 风格的无线资源分配环境，管理信道状态、执行动作、计算 QoE。 + +```python +class WirelessEnv: + def __init__(self, config: dict) +``` + +| 属性 | 类型 | 说明 | +|---|---|---| +| `obs_dim` | int (property) | 观察维度 = N + 4 | +| `act_dim` | int (property) | 动作维度 = 3 | +| `N` | int | 子载波数量 | +| `K_s`, `K_b`, `K` | int | 语义/传统/总用户数 | + +#### 方法 + +**`reset() -> (obs_s, obs_b)`** + +重置环境。随机化用户距离、信道、辅助参数。 + +| 返回 | 类型 | 说明 | +|---|---|---| +| `obs_s` | np.ndarray (obs_dim,) | 语义 agent 观察 (float32) | +| `obs_b` | np.ndarray (obs_dim,) | 传统 agent 观察 (float32) | + +--- + +**`step(action_s, action_b) -> (obs_s, obs_b, reward_s, reward_b, done, info)`** + +执行一步。 + +| 参数 | 类型 | 说明 | +|---|---|---| +| `action_s` | np.ndarray (3,) | 语义 agent 动作 [sub_frac, power_frac, rho] | +| `action_b` | np.ndarray (3,) | 传统 agent 动作 [sub_frac, power_frac, _] | + +| 返回 | 类型 | 说明 | +|---|---|---| +| `obs_s`, `obs_b` | np.ndarray | 新观察 | +| `reward_s`, `reward_b` | float | 各自平均 QoE（作为基础奖励） | +| `done` | bool | 是否达到 max_steps | +| `info` | dict | 详细信息（见下表） | + +**info 字典内容:** + +| Key | 类型 | 说明 | +|---|---|---| +| `qoe_semantic` | float | 语义组平均 QoE | +| `qoe_traditional` | float | 传统组平均 QoE | +| `qoe_sys` | float | 系统平均 QoE | +| `qoe_list` | list[float] | 每个用户的 QoE | +| `rates` | list[float] | 传统用户速率 (bps) | +| `ssim_values` | list[float] | 语义用户 SSim 值 | +| `rate_satisfaction` | float | 速率满足比例 ∈ [0, 1] | +| `rho` | float | 实际使用的压缩率 | +| `n_sub_s`, `n_sub_b` | int | 分配的子载波数量 | + +--- + +## 2. 算法模块 agents/ + +### Actor + +**文件**: `agents/actor.py` + +确定性策略网络，输出 [0, 1] 范围的连续动作。 + +```python +class Actor(nn.Module): + def __init__(self, obs_dim: int, act_dim: int, hidden_sizes: list = [256, 256, 128]) +``` + +**`forward(obs) -> torch.Tensor`** + +| 参数 | 类型 | 说明 | +|---|---|---| +| `obs` | Tensor (batch, obs_dim) | 观察 | +| **返回** | Tensor (batch, act_dim) | 动作 ∈ [0, 1]，通过 `(tanh(x) + 1) / 2` | + +--- + +### Critic + +**文件**: `agents/critic.py` + +联合 Q 值网络 (CTDE)，输入所有 agent 的观察和动作。 + +```python +class Critic(nn.Module): + def __init__(self, obs_dim_total: int, act_dim_total: int, hidden_sizes: list = [512, 512, 256]) +``` + +- `obs_dim_total` = obs_dim × 2 = 136 +- `act_dim_total` = act_dim × 2 = 6 +- 总输入维度 = 142 + +**`forward(obs, act) -> torch.Tensor`** + +| 参数 | 类型 | 说明 | +|---|---|---| +| `obs` | Tensor (batch, obs_dim_total) | 联合观察 concat(obs_s, obs_b) | +| `act` | Tensor (batch, act_dim_total) | 联合动作 concat(act_s, act_b) | +| **返回** | Tensor (batch, 1) | Q 值 | + +--- + +### OUNoise + +**文件**: `agents/noise.py` + +Ornstein-Uhlenbeck 探索噪声，带线性 sigma 衰减。 + +```python +class OUNoise: + def __init__(self, size: int, mu: float = 0.0, theta: float = 0.15, + sigma_init: float = 0.2, sigma_min: float = 0.01, decay_period: int = 5000) +``` + +| 参数 | 说明 | +|---|---| +| `size` | 噪声维度 (= act_dim = 3) | +| `theta` | 回归速率 (默认 0.15) | +| `sigma_init` | 初始标准差 (默认 0.2) | +| `sigma_min` | 最小标准差 (默认 0.01) | +| `decay_period` | 线性衰减周期 (默认 5000 episodes) | + +#### 方法 + +| 方法 | 说明 | +|---|---| +| `reset()` | 重置噪声状态到 μ | +| `sample() -> np.ndarray` | 采样一步 OU 噪声 | +| `decay_sigma(episode)` | 线性衰减 sigma: `σ = max(σ_min, σ_init - (σ_init - σ_min) · episode / decay_period)` | + +--- + +### ReplayBuffer + +**文件**: `agents/replay_buffer.py` + +9-field 经验回放缓冲区。 + +```python +class ReplayBuffer: + def __init__(self, capacity: int = 100000) +``` + +#### 方法 + +**`push(obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, done)`** + +存储一个 transition。所有参数为 numpy array 或 float。 + +**`sample(batch_size) -> dict`** + +随机采样一批 transitions。 + +| 返回 key | 类型 | Shape | +|---|---|---| +| `obs_s` | np.ndarray | (batch, obs_dim) | +| `obs_b` | np.ndarray | (batch, obs_dim) | +| `act_s` | np.ndarray | (batch, act_dim) | +| `act_b` | np.ndarray | (batch, act_dim) | +| `rew_s` | np.ndarray | (batch, 1) | +| `rew_b` | np.ndarray | (batch, 1) | +| `next_obs_s` | np.ndarray | (batch, obs_dim) | +| `next_obs_b` | np.ndarray | (batch, obs_dim) | +| `done` | np.ndarray | (batch, 1) | + +**`__len__() -> int`**: 当前存储的 transition 数量。 + +--- + +### CoMADDPG + +**文件**: `agents/co_maddpg.py` + +Co-MADDPG 主算法，实现 Stackelberg Leader-Follower 更新。 + +```python +class CoMADDPG: + def __init__(self, config: dict) +``` + +| 关键属性 | 类型 | 说明 | +|---|---|---| +| `actor_s`, `actor_b` | Actor | 语义/传统 Actor 网络 | +| `critic_s`, `critic_b` | Critic | 语义/传统 Critic 网络 | +| `actor_s_target`, ... | Actor/Critic | Target 网络 (4个) | +| `noise_s`, `noise_b` | OUNoise | 探索噪声 | +| `buffer` | ReplayBuffer | 经验回放 | +| `current_lambda` | float | 当前 λ(t) 值 | +| `device` | torch.device | 计算设备 | + +#### 方法 + +**`select_action(obs_s, obs_b, explore=True) -> (act_s, act_b)`** + +| 参数 | 说明 | +|---|---| +| `obs_s`, `obs_b` | np.ndarray (obs_dim,) — 各 agent 观察 | +| `explore` | bool — 是否添加 OU 噪声 | +| **返回** | tuple(np.ndarray, np.ndarray) — 动作 ∈ [0, 1]³ | + +--- + +**`compute_rewards(info) -> (rew_s, rew_b)`** + +根据 info 字典计算混合奖励。内部更新 `self.current_lambda`。 + +| 参数 | 说明 | +|---|---| +| `info` | dict — 来自 env.step() | +| **返回** | tuple(float, float) — 混合奖励 | + +奖励公式: +``` +r_coop_i = coop_self·qoe_i + coop_other·qoe_j + coop_sys·qoe_sys +r_comp_i = comp_self·qoe_i + comp_sys·qoe_sys +r_i = λ·r_coop_i + (1-λ)·r_comp_i +``` + +--- + +**`update() -> dict`** + +执行 Stackelberg 更新。返回 loss 字典。 + +| 返回 key | 说明 | +|---|---| +| `critic_loss_b` | Follower Critic 损失 | +| `actor_loss_b` | Follower Actor 损失 | +| `critic_loss_s` | Leader Critic 损失 | +| `actor_loss_s` | Leader Actor 损失 | +| `lambda` | 当前 λ(t) | + +--- + +**`save(path)` / `load(path)`** + +保存/加载所有网络参数到指定目录。 + +| 文件 | 内容 | +|---|---| +| `model_s.pth` | Actor S + Critic S + 对应 Target 网络 | +| `model_b.pth` | Actor B + Critic B + 对应 Target 网络 | + +--- + +## 3. 基线模块 baselines/ + +### 通用接口 / Common Interface + +所有 7 个基线实现与 CoMADDPG 相同的接口： + +```python +def __init__(self, config: dict) +def select_action(obs_s, obs_b, explore=True) -> (act_s, act_b) +def compute_rewards(info) -> (rew_s, rew_b) +def update() -> dict or None +def save(path) +def load(path) + +# 属性 +self.buffer: ReplayBuffer # 或等效 +self.noise_s: OUNoise # 部分基线有 (用于 train.py 的 hasattr 检查) +self.noise_b: OUNoise +``` + +### 各基线差异 / Baseline Differences + +| 基线类 | 文件 | λ | 更新方式 | Critic | 特殊类 | +|---|---|---|---|---|---| +| `PureCooperative` | `pure_coop.py` | 1.0 固定 | Simultaneous | Joint | — | +| `PureCompetitive` | `pure_comp.py` | 0.0 固定 | Simultaneous | Joint | — | +| `FixedLambda` | `fixed_lambda.py` | 0.5 固定 | Stackelberg | Joint | — | +| `IndependentDDPG` | `iddpg.py` | 0.0 | Simultaneous | Independent | `IndependentCritic` | +| `SingleAgentDQN` | `single_dqn.py` | 0.5 | N/A (集中) | Centralized | `DQNNet`, `DQNReplayBuffer`, `EpsilonAdapter` | +| `EqualAllocation` | `equal_alloc.py` | 0.5 | N/A (无学习) | None | `DummyBuffer` | +| `SemanticOnly` | `semantic_only.py` | 1.0 | N/A (单策略) | Single | `SemanticCritic`, `SemanticBuffer` | + +#### 特殊说明 + +**SingleAgentDQN**: 48 个离散动作 = 4 (sub_levels) × 4 (power_levels) × 3 (rho_levels)。使用 `EpsilonAdapter` 适配 `noise_s.decay_sigma()` 接口。 + +**EqualAllocation**: 无学习，永远输出 `[0.5, 0.5, 0.5]`。`DummyBuffer` 有 `push()` 和 `__len__()` 但不存储数据。 + +**IndependentDDPG**: `IndependentCritic` 输入为单个 agent 的 `(obs, act)` 而非联合输入，消融 CTDE 的效果。 + +--- + +## 4. 工具模块 utils/ + +### metrics.py + +**文件**: `utils/metrics.py` + +#### 函数 + +**`jain_fairness(values) -> float`** + +Jain 公平性指数。`J = (Σx_i)² / (n·Σx_i²)`, 范围 [1/n, 1]。 + +--- + +**`rate_satisfaction(rates, min_rate) -> float`** + +速率满足比例。满足 `R_k ≥ R_req` 的用户占比。 + +--- + +**`compute_system_qoe(qoe_list) -> float`** + +系统级 QoE = 所有用户 QoE 的均值。 + +--- + +**`compute_lambda(qoe_sys, beta=5.0, q_threshold=0.6) -> float`** + +动态协作权重。`λ = 1 / (1 + exp(-β·(QoE_sys - Q_th)))` + +--- + +**`compute_mixed_reward(qoe_s, qoe_b, qoe_sys, lam, reward_config) -> (float, float)`** + +计算混合奖励。`r_i = λ·r_coop_i + (1-λ)·r_comp_i` + +--- + +**`moving_average(data, window) -> np.ndarray`** + +滑动平均平滑。 + +--- + +### visualization.py + +**文件**: `utils/visualization.py` + +IEEE 风格绘图工具，对应论文 Section VII 的 12 张图。 + +```python +class Plotter: + def __init__(self, save_dir: str = "results/figures") +``` + +#### ALGO_STYLES + +内置样式字典，为 8 个算法分配颜色、标记、线型： + +```python +ALGO_STYLES = { + "Co-MADDPG": {"color": "#E74C3C", "marker": "o", "linestyle": "-"}, + "PureCooperative": {"color": "#3498DB", "marker": "s", "linestyle": "--"}, + "PureCompetitive": {"color": "#2ECC71", "marker": "^", "linestyle": "--"}, + ... +} +``` + +#### 绘图方法 + +| 方法 | 对应图表 | 参数 | +|---|---|---| +| `plot_convergence(data)` | Fig.2 | `{algo: [episode_rewards]}` | +| `plot_qoe_vs_snr(data)` | Fig.3 | `{algo: {snr: qoe}}` | +| `plot_fairness_vs_snr(data)` | Fig.4 | `{algo: {snr: fairness}}` | +| `plot_qoe_vs_users(data)` | Fig.5 | `{algo: {n_users: qoe}}` | +| `plot_rate_satisfaction(data)` | Fig.6 | `{algo: {n_users: rate_sat}}` | +| `plot_lambda_trajectory(lambdas)` | Fig.7 | `[λ_1, λ_2, ...]` | +| `plot_lambda_qoe_scatter(lambdas, qoes)` | Fig.8 | 两个等长列表 | +| `plot_qoe_vs_semantic_ratio(data)` | Fig.9 | `{algo: {ratio: qoe}}` | +| `plot_ablation(data)` | Fig.10 | `{algo: qoe_mean}` | +| `plot_beta_sensitivity(data)` | Fig.11 | `{beta: qoe}` | +| `plot_qth_sensitivity(data)` | Fig.12 | `{qth: qoe}` | + +所有方法自动保存 PNG (300 DPI) 到 `save_dir`。 + +--- + +## 5. 入口脚本 / Entry Scripts + +### train.py + +训练入口，支持 CLI 参数。 + +```bash +python train.py [--algo ALGO] [--config PATH] [--episodes N] [--steps N] [--seed N] +``` + +| 参数 | 默认值 | 说明 | +|---|---|---| +| `--algo` | `co_maddpg` | 算法名 (`co_maddpg`, `pure_coop`, `all`, 等) | +| `--config` | `configs/default.yaml` | 配置文件路径 | +| `--episodes` | 从配置读取 (5000) | 训练轮数 | +| `--steps` | 从配置读取 (200) | 每轮步数 | +| `--seed` | 从配置读取 (42) | 随机种子 | + +**关键函数:** + +- `load_config(path)` — 加载 YAML +- `get_algorithm(name, config)` — 工厂函数，返回算法实例 +- `train_single(algo_name, config)` — 训练单个算法 +- `train_all(config)` — 训练全部 8 个算法 + +--- + +### evaluate.py + +评估入口，运行 8 个场景生成 12+ 张图。 + +```bash +python evaluate.py [--results_dir PATH] [--config PATH] +``` + +**8 个评估场景:** + +| # | 函数 | 说明 | +|---|---|---| +| 1 | `scenario_convergence()` | 绘制训练收敛曲线 | +| 2 | `scenario_qoe_vs_snr()` | 扫描 SNR (通过调节 noise_psd) | +| 3 | `scenario_fairness_vs_snr()` | 不同 SNR 下的公平性 | +| 4 | `scenario_qoe_vs_users()` | 扫描用户数量 | +| 5 | `scenario_rate_satisfaction()` | 不同用户数下的速率满足度 | +| 6 | `scenario_lambda_dynamics()` | λ(t) 时间演化 | +| 7 | `scenario_ablation()` | 消融实验对比 | +| 8 | `scenario_sensitivity()` | β 和 Q_th 敏感性 | + +--- + +## 类型约定 / Type Conventions + +| 约定 | 说明 | +|---|---| +| 所有观察/动作 | numpy float32 | +| 神经网络输入 | torch.FloatTensor (自动转换) | +| 配置参数 | 从 YAML 加载，保持原始类型 | +| 奖励/QoE | Python float | +| 信道增益 | numpy complex128 | +| 布尔 done | Python bool | diff --git a/code/ARCHITECTURE.md b/code/ARCHITECTURE.md new file mode 100644 index 0000000..765bc53 --- /dev/null +++ b/code/ARCHITECTURE.md @@ -0,0 +1,342 @@ +# 架构设计文档 / Architecture Document + +## 系统全局视图 / System Overview + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ train.py (入口) │ +│ CLI 解析 → 加载配置 → 初始化环境+算法 → 训练循环 → 保存模型 │ +└──────────┬──────────────────────────────────┬───────────────────┘ + │ │ + ▼ ▼ +┌─────────────────────┐ ┌──────────────────────────┐ +│ envs/ (环境层) │ │ agents/ + baselines/ │ +│ │ │ (算法层) │ +│ WirelessEnv │◄────────►│ CoMADDPG / 7 baselines │ +│ ├─ ChannelModel │ obs, │ ├─ Actor (策略网络) │ +│ ├─ SemanticModule │ reward, │ ├─ Critic (价值网络) │ +│ └─ step/reset │ done │ ├─ ReplayBuffer │ +└─────────────────────┘ │ └─ OUNoise │ + └──────────────────────────┘ + │ + ▼ + ┌──────────────────────────┐ + │ utils/ (工具层) │ + │ metrics.py (评估指标) │ + │ visualization.py (绘图) │ + └──────────────────────────┘ + │ + ▼ + ┌──────────────────────────┐ + │ evaluate.py (评估入口) │ + │ 8 场景 × 12+ 张图 │ + └──────────────────────────┘ +``` + +--- + +## 模块依赖关系 / Module Dependencies + +``` +configs/default.yaml + │ + ├──► envs/channel_model.py (读取 env.carrier_freq, env.noise_psd, env.subcarrier_spacing) + ├──► envs/semantic_module.py (读取 env.rho_max, env.rho_min, env.w1, env.w2) + ├──► envs/wireless_env.py (读取 env.* 和 training.max_steps) + │ ├── uses ChannelModel + │ └── uses SemanticModule + │ + ├──► agents/co_maddpg.py (读取 env.num_subcarriers, training.*, network.*, reward.*) + │ ├── uses Actor (agents/actor.py) + │ ├── uses Critic (agents/critic.py) + │ ├── uses ReplayBuffer (agents/replay_buffer.py) + │ └── uses OUNoise (agents/noise.py) + │ + ├──► baselines/*.py (各基线复用 Actor, Critic, ReplayBuffer, OUNoise) + │ + └──► utils/metrics.py (读取 reward.* 权重) + utils/visualization.py (独立，无配置依赖) +``` + +--- + +## 数据流 / Data Flow + +### 训练循环（单个 Episode） + +``` + ┌─── Episode 开始 ───┐ + │ │ + ▼ │ + env.reset() │ + → (obs_s, obs_b) │ + │ │ + ┌───► Step 循环 (200 步) ◄────┐ │ + │ │ │ │ + │ agent.select_action │ │ + │ (obs_s) → act_s │ │ + │ (obs_b) → act_b │ │ + │ │ │ │ + │ env.step(act_s, act_b) │ │ + │ → (obs_s', obs_b', │ │ + │ rew_s, rew_b, │ │ + │ done, info) │ │ + │ │ │ │ + │ agent.compute_rewards │ │ + │ (info) → (r_s, r_b) │ │ + │ │ │ │ + │ buffer.push( │ │ + │ obs_s, obs_b, │ │ + │ act_s, act_b, │ │ + │ r_s, r_b, │ │ + │ obs_s', obs_b', │ │ + │ done) │ │ + │ │ │ │ + │ agent.update() │ │ + │ │ │ │ + │ not done ────────────────┘ │ + │ │ + └─── done ─── noise.decay() ──────┘ + │ + save model + log +``` + +### 环境 step() 内部流程（8 步） + +``` +Action Decode Subcarrier Alloc Power Alloc +(act_s, act_b) → Greedy by channel → Equal within group + │ │ │ + ▼ ▼ ▼ + n_sub_s, n_sub_b sem_subs, trad_subs power_matrix (K×N) + │ │ + ▼ ▼ + ┌─── SNR = p·|h|²/σ² ───┐ + │ │ + ┌─────────┴──────┐ ┌──────────┴──────┐ + │ Traditional │ │ Semantic │ + │ Rate = Σ Δf· │ │ avg_SNR → │ + │ log₂(1+γ) │ │ SSim(γ̄, ρ) → │ + │ QoE_b = min │ │ QoE_s = w1·SSim │ + │ (R/R_req, 1) │ │ + w2·(1-ρ/ρ_max)│ + └───────┬────────┘ └────────┬─────────┘ + │ │ + └────────┬───────────────┘ + ▼ + QoE_sys = mean(all QoE) + │ + Regenerate channel (block fading) + │ + Build (obs_s', obs_b', rew_s, rew_b, done, info) +``` + +--- + +## Co-MADDPG Stackelberg 更新机制 / Stackelberg Update Mechanism + +这是本算法的核心创新。更新顺序体现了 Leader-Follower 博弈结构： + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ Stackelberg Update │ +│ │ +│ PHASE 1: 更新 Follower (Agent B) / Update Follower First │ +│ ┌───────────────────────────────────────────────────────┐ │ +│ │ 1. Critic B: L_B = (Q_B(s,a) - y_B)² │ │ +│ │ y_B = r_B + γ·Q_B'(s', a_s'_target, a_b'_target) │ │ +│ │ 2. Actor B: max Q_B(s, a_s_current, π_B(o_b)) │ │ +│ │ 3. Soft update: θ_B_target ← τ·θ_B + (1-τ)·θ_B_tgt │ │ +│ └───────────────────────────────────────────────────────┘ │ +│ │ │ +│ ▼ (B's policy is now updated) │ +│ │ +│ PHASE 2: 更新 Leader (Agent S) / Update Leader with B's BR │ +│ ┌───────────────────────────────────────────────────────┐ │ +│ │ 1. Get B's best response: a_b_br = π_B_updated(o_b) │ │ +│ │ .detach() — 不反传梯度给 B │ │ +│ │ 2. Critic S: L_S = (Q_S(s, a) - y_S)² │ │ +│ │ 3. Actor S: max Q_S(s, π_S(o_s), a_b_br) │ │ +│ │ Leader 优化时考虑了 Follower 的最优响应 │ │ +│ │ 4. Soft update: θ_S_target ← τ·θ_S + (1-τ)·θ_S_tgt │ │ +│ └───────────────────────────────────────────────────────┘ │ +│ │ +│ PHASE 3: 更新动态 λ / Update Dynamic λ │ +│ ┌───────────────────────────────────────────────────────┐ │ +│ │ λ(t) = sigmoid(β · (QoE_sys - Q_th)) │ │ +│ │ β=5.0 控制切换陡度，Q_th=0.6 为切换阈值 │ │ +│ └───────────────────────────────────────────────────────┘ │ +└─────────────────────────────────────────────────────────────────┘ +``` + +--- + +## 奖励计算流程 / Reward Computation Flow + +``` + env.step() 输出 info dict + │ + ┌─────────┴─────────┐ + │ qoe_semantic │ + │ qoe_traditional │ + │ qoe_sys │ + └─────────┬─────────┘ + │ + agent.compute_rewards(info) + │ + ┌────────────┴────────────┐ + ▼ ▼ + r_coop_s = r_coop_b = + 0.5·qoe_s + 0.5·qoe_b + + 0.3·qoe_b + 0.3·qoe_s + + 0.2·qoe_sys 0.2·qoe_sys + │ │ + r_comp_s = r_comp_b = + 0.8·qoe_s + 0.8·qoe_b + + 0.2·qoe_sys 0.2·qoe_sys + │ │ + └────────────┬────────────┘ + │ + λ = sigmoid(β·(qoe_sys - Q_th)) + │ + r_s = λ·r_coop_s + (1-λ)·r_comp_s + r_b = λ·r_coop_b + (1-λ)·r_comp_b +``` + +--- + +## 观察空间与动作空间 / Observation & Action Spaces + +### 观察空间 (obs_dim = N + 4 = 68) + +| 维度 | 内容 (语义 Agent S) | 内容 (传统 Agent B) | +|---|---|---| +| [0 : N] | 语义用户平均信道功率 (归一化) | 传统用户平均信道功率 (归一化) | +| [N] | qoe_avg_s (滚动平均 QoE) | qoe_avg_b (滚动平均 QoE) | +| [N+1] | content_sensitivity | business_priority | +| [N+2] | alloc_s (当前子载波分配比) | alloc_b (当前子载波分配比) | +| [N+3] | load_s (流量负载) | load_b (流量负载) | + +### 动作空间 (act_dim = 3, 连续 [0,1]) + +| 维度 | 含义 (语义 Agent S) | 含义 (传统 Agent B) | +|---|---|---| +| [0] | 请求子载波比例 n_sub_frac | 请求子载波比例 n_sub_frac | +| [1] | 功率分配比例 p_frac | 功率分配比例 p_frac | +| [2] | 压缩率 ρ (映射到 [ρ_min, ρ_max]) | 冗余参数 (未使用) | + +--- + +## 网络架构 / Network Architecture + +### Actor Network + +``` +Input: obs (68,) + │ + ├─ Linear(68 → 256) + ReLU + ├─ Linear(256 → 256) + ReLU + ├─ Linear(256 → 128) + ReLU + ├─ Linear(128 → 3) + └─ (Tanh + 1) / 2 → output ∈ [0, 1]³ +``` + +### Critic Network (Joint, CTDE) + +``` +Input: concat(obs_s, obs_b, act_s, act_b) = (142,) + │ + ├─ Linear(142 → 512) + ReLU + ├─ Linear(512 → 512) + ReLU + ├─ Linear(512 → 256) + ReLU + └─ Linear(256 → 1) → Q-value (scalar) +``` + +--- + +## 经验回放 / Replay Buffer + +9-field transitions: + +``` +Transition = (obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, done) + (68,) (68,) (3,) (3,) (1,) (1,) (68,) (68,) (1,) +``` + +- 容量: 100,000 transitions +- 采样: 均匀随机采样 batch_size=256 +- 存储: deque 结构，FIFO 淘汰 + +--- + +## Agent 接口契约 / Agent Interface Contract + +所有 8 个算法必须实现以下接口，以兼容 `train.py` 的训练循环： + +```python +class AgentInterface: + # 必须有以下属性之一 + self.buffer: ReplayBuffer # 优先检查 + self.replay_buffer: ReplayBuffer # 备选 + + # 选择动作 + def select_action(obs_s, obs_b, explore=True) -> (act_s, act_b) + + # 计算奖励 + def compute_rewards(info) -> (rew_s, rew_b) + + # 更新网络 + def update() -> dict or None + + # 保存/加载模型 + def save(path) + def load(path) + + # 噪声衰减 (可选，train.py 通过 hasattr 检查) + self.noise_s: OUNoise # 需有 decay_sigma(episode) 方法 + self.noise_b: OUNoise +``` + +--- + +## 信道模型 / Channel Model + +基于 3GPP Urban Micro (UMi) NLOS 模型： + +``` +距离: d ~ U(50, 500) 米 +路径损耗: PL(d) = 36.7·log₁₀(d) + 22.7 + 26·log₁₀(fc) [dB] +信道增益: h_{k,n} ~ CN(0, 10^{-PL/10}) (Rayleigh fading) +噪声功率: σ² = 10^{(N₀_dBm - 30)/10} · Δf [W] +SNR: γ_{k,n} = p_{k,n} · |h_{k,n}|² / σ² +``` + +- 块衰落模型 (Block fading): 每个 step 重新生成信道 +- K_s=3 语义用户 + K_b=3 传统用户 = 6 users +- N=64 个 OFDM 子载波 +- 子载波分配: 贪婪算法 (语义用户优先) +- 功率分配: 组内均分 + +--- + +## 设计决策 / Design Decisions + +### 1. 为什么用 Stackelberg 而不是 Nash？ +- Stackelberg 适合异构场景：语义用户 (Leader) 先决策，传统用户 (Follower) 最优响应 +- 保证了均衡存在性（定理 1-2 在论文中证明） + +### 2. 为什么 λ(t) 用 sigmoid？ +- 连续可微，适合梯度训练 +- β 参数控制切换陡度，Q_th 控制切换点 +- 系统 QoE 高时偏合作 (λ→1)，低时偏竞争 (λ→0) + +### 3. 为什么观察空间包含额外 4 维？ +- 仅信道信息不够：agent 需要知道当前 QoE 水平、流量负载、分配状况 +- 这些额外信息帮助 agent 做出更有环境感知的决策 + +### 4. 为什么 Critic 是联合的 (CTDE)？ +- 集中训练时可访问所有信息，解决非平稳性问题 +- 分散执行时只用各自的 Actor，降低通信开销 + +### 5. 为什么语义用户优先分配子载波？ +- 体现 Leader 的先动优势 (First-mover advantage) +- 与 Stackelberg 博弈结构一致 diff --git a/code/README.md b/code/README.md new file mode 100644 index 0000000..1ed7502 --- /dev/null +++ b/code/README.md @@ -0,0 +1,286 @@ +# Co-MADDPG: 面向语义与传统混合通信的合作竞争多智能体资源分配框架 + +**Co-MADDPG: Cooperative-Competitive Multi-Agent Resource Allocation for Semantic-Traditional Hybrid Wireless Communication** + +--- + +## 项目简介 / Project Overview + +本项目实现了 Co-MADDPG 算法——一种基于 Stackelberg 博弈和动态合作-竞争切换机制的多智能体深度强化学习框架，用于语义通信与传统比特级通信共存场景下的 OFDMA 无线资源分配。 + +This project implements the Co-MADDPG algorithm — a multi-agent deep reinforcement learning framework based on Stackelberg game dynamics and dynamic cooperation-competition switching for OFDMA wireless resource allocation in semantic-traditional hybrid communication systems. + +### 核心创新 / Key Innovations + +1. **合作竞争博弈建模 / Coopetition Game Modeling**: 将语义用户 (Leader) 与传统用户 (Follower) 之间的资源竞争建模为 Stackelberg 博弈 +2. **动态 λ(t) 切换 / Dynamic λ(t) Switching**: `λ(t) = sigmoid(β·(QoE_sys - Q_th))`，根据系统 QoE 在合作与竞争之间自适应切换 +3. **异构 QoE 指标 / Heterogeneous QoE**: 语义用户使用 SSim + 压缩率，传统用户使用速率满足度 +4. **CTDE 架构 / CTDE Architecture**: 集中训练分散执行，联合 Critic 网络 + +### 目标期刊 / Target Venue + +IEEE Transactions on Communications (TCOM) + +--- + +## 环境要求 / Requirements + +### Python 版本 / Python Version +- Python 3.8+ + +### 依赖库 / Dependencies + +```bash +pip install numpy torch pyyaml matplotlib +``` + +| 库 / Library | 版本 / Version | 用途 / Purpose | +|---|---|---| +| `numpy` | ≥1.20 | 数值计算、信道建模 / Numerical computation, channel modeling | +| `torch` | ≥1.10 (CPU 或 GPU) | 神经网络训练 / Neural network training | +| `pyyaml` | ≥5.0 | 配置文件加载 / Configuration file loading | +| `matplotlib` | ≥3.4 | IEEE 风格绘图 / IEEE-style plotting | + +### 硬件建议 / Hardware Recommendations + +| 场景 / Scenario | 配置 / Configuration | +|---|---| +| 功能验证 (Smoke Test) | CPU, 2-5 episodes, ~30 秒 | +| 短期训练 (Short Training) | CPU/GPU, 100-500 episodes, ~10-60 分钟 | +| 完整训练 (Full Training) | GPU (CUDA), 5000 episodes, ~2-8 小时 | + +--- + +## 快速开始 / Quick Start + +### 1. 克隆项目 / Clone + +```bash +git clone +cd SemantiCommunication/code +``` + +### 2. 功能验证 (Smoke Test) + +```bash +# 训练 Co-MADDPG 2 个 episode（验证代码逻辑） +python train.py --algo co_maddpg --episodes 2 --steps 10 + +# 训练所有 8 个算法各 2 个 episode +python train.py --algo all --episodes 2 --steps 10 +``` + +### 3. 正式训练 / Full Training + +```bash +# 单算法训练（推荐先跑主算法） +python train.py --algo co_maddpg --episodes 5000 + +# 训练全部 8 个算法 +python train.py --algo all --episodes 5000 + +# 指定配置文件 +python train.py --algo co_maddpg --config configs/default.yaml --episodes 5000 +``` + +### 4. 评估与绘图 / Evaluation & Plotting + +```bash +# 运行全部 8 个评估场景，生成 12+ 张图 +python evaluate.py + +# 指定结果目录 +python evaluate.py --results_dir results/ +``` + +--- + +## 支持的算法 / Supported Algorithms + +| # | 算法 / Algorithm | CLI 名称 | λ | 更新方式 / Update | Critic 类型 | 用途 / Purpose | +|---|---|---|---|---|---|---| +| 1 | **Co-MADDPG** | `co_maddpg` | 动态 dynamic | Stackelberg | Joint (CTDE) | 本文提出 / Proposed | +| 2 | PureCooperative | `pure_coop` | 1.0 | Simultaneous | Joint | 消融：去除竞争 / Ablate competition | +| 3 | PureCompetitive | `pure_comp` | 0.0 | Simultaneous | Joint | 消融：去除合作 / Ablate cooperation | +| 4 | FixedLambda | `fixed_lambda` | 0.5 | Stackelberg | Joint | 消融：去除动态 λ / Ablate dynamic λ | +| 5 | IDDPG | `iddpg` | 0.0 | Simultaneous | Independent | 消融：去除 CTDE / Ablate CTDE | +| 6 | SingleAgentDQN | `single_dqn` | 0.5 | N/A | Centralized | 非 MARL 基线 / Non-MARL baseline | +| 7 | EqualAllocation | `equal_alloc` | 0.5 | N/A | None | 性能下界 / Lower bound | +| 8 | SemanticOnly | `semantic_only` | 1.0 | N/A | Single | 消融：去除异构性 / Ablate heterogeneity | + +--- + +## 项目结构 / Project Structure + +``` +SemantiCommunication/code/ +│ +├── configs/ # 配置文件 / Configuration +│ ├── __init__.py +│ └── default.yaml # 主配置（超参数、环境参数）/ Main config +│ +├── envs/ # 环境模块 / Environment modules +│ ├── __init__.py +│ ├── channel_model.py # 3GPP 信道模型 / 3GPP channel model (Eq.5-8) +│ ├── semantic_module.py # 语义相似度 SSim / Semantic similarity (SSim) +│ └── wireless_env.py # Gym 风格无线环境 / Gym-like wireless env +│ +├── agents/ # 核心算法 / Core algorithm +│ ├── __init__.py +│ ├── actor.py # Actor 网络 FC→Tanh→[0,1] +│ ├── critic.py # Critic 网络 (Joint Q-value) +│ ├── noise.py # OU 探索噪声 / OU exploration noise +│ ├── replay_buffer.py # 9-field 经验回放 / 9-field replay buffer +│ └── co_maddpg.py # Co-MADDPG 主算法 / Main algorithm (★) +│ +├── baselines/ # 7 个基线算法 / 7 baseline algorithms +│ ├── __init__.py +│ ├── pure_coop.py # λ=1 纯协作 +│ ├── pure_comp.py # λ=0 纯竞争 +│ ├── fixed_lambda.py # λ=0.5 固定 +│ ├── iddpg.py # 独立 DDPG (无 CTDE) +│ ├── single_dqn.py # 集中式 DQN +│ ├── equal_alloc.py # 均分分配 +│ └── semantic_only.py # 仅语义 DDPG +│ +├── utils/ # 工具模块 / Utility modules +│ ├── __init__.py +│ ├── metrics.py # 评估指标 (Jain fairness, λ, rewards) +│ └── visualization.py # IEEE 风格绘图 (12 种图) +│ +├── train.py # 训练入口 / Training entry point (★) +├── evaluate.py # 评估入口 / Evaluation entry point (★) +├── README.md # 本文件 / This file +├── ARCHITECTURE.md # 架构设计文档 / Architecture document +├── API.md # API 接口文档 / API reference +└── results/ # 训练结果输出 / Training output directory +``` + +--- + +## 配置说明 / Configuration + +配置文件位于 `configs/default.yaml`，主要分为 4 个部分： + +### env（环境参数） + +| 参数 | 默认值 | 说明 | +|---|---|---| +| `num_subcarriers` | 64 | OFDMA 子载波数 N | +| `bandwidth` | 10.0e+6 | 系统带宽 (Hz) | +| `subcarrier_spacing` | 156250.0 | 子载波间隔 Δf (Hz) | +| `max_power` | 1.0 | 最大发射功率 (W) | +| `noise_psd` | -174 | 噪声功率谱密度 (dBm/Hz) | +| `carrier_freq` | 3.5 | 载波频率 (GHz) | +| `num_semantic_users` | 3 | 语义用户数 K_s | +| `num_traditional_users` | 3 | 传统用户数 K_b | +| `min_rate_req` | 5.0e+5 | 传统用户最低速率需求 (bps) | +| `rho_max` / `rho_min` | 1.0 / 0.05 | 压缩率范围 | +| `w1` / `w2` | 0.7 / 0.3 | 语义 QoE 权重 | + +### training（训练参数） + +| 参数 | 默认值 | 说明 | +|---|---|---| +| `max_episodes` | 5000 | 最大训练轮数 | +| `max_steps` | 200 | 每轮最大步数 | +| `batch_size` | 256 | 批量大小 | +| `buffer_capacity` | 100000 | 经验回放容量 | +| `actor_lr` / `critic_lr` | 1e-4 / 3e-4 | 学习率 | +| `gamma` | 0.95 | 折扣因子 | +| `tau` | 0.01 | 软更新系数 | +| `beta` | 5.0 | λ(t) sigmoid 的陡度 | +| `q_threshold` | 0.6 | λ(t) 切换阈值 Q_th | + +### network（网络结构） + +| 参数 | 默认值 | 说明 | +|---|---|---| +| `actor_hidden` | [256, 256, 128] | Actor 隐藏层 | +| `critic_hidden` | [512, 512, 256] | Critic 隐藏层 | + +### reward（奖励权重） + +| 参数 | 默认值 | 说明 | +|---|---|---| +| `coop_self` / `coop_other` / `coop_sys` | 0.5 / 0.3 / 0.2 | 合作奖励权重 | +| `comp_self` / `comp_sys` | 0.8 / 0.2 | 竞争奖励权重 | + +--- + +## 关键公式 / Key Formulas + +| 公式 | 表达式 | 论文编号 | +|---|---|---| +| 路径损耗 / Path Loss | `PL(d) = 36.7·log₁₀(d) + 22.7 + 26·log₁₀(fc)` | Eq.(5) | +| 信道增益 / Channel Gain | `h_{k,n} ~ CN(0, 10^{-PL/10})` | Eq.(6) | +| 噪声功率 / Noise Power | `σ² = 10^{(N₀_dBm-30)/10} · Δf` | Eq.(7) | +| 信噪比 / SNR | `γ_{k,n} = p_{k,n} · \|h_{k,n}\|² / σ²` | Eq.(8) | +| 语义相似度 / SSim | `φ(γ̄,ρ) = 1 - exp(-a(ρ)·γ̄^{b(ρ)})` | — | +| 语义 QoE | `QoE_s = 0.7·SSim + 0.3·(1-ρ/ρ_max)` | — | +| 传统 QoE | `QoE_b = min(R_k/R_req, 1)` | — | +| 动态 λ | `λ(t) = sigmoid(β·(QoE_sys - Q_th))` | — | +| 混合奖励 | `r_i = λ·r_coop + (1-λ)·r_comp` | — | + +--- + +## 评估场景 / Evaluation Scenarios + +`evaluate.py` 包含 8 个评估场景，对应论文 Section VII 的 12 张图： + +| # | 场景 | 对应图表 | 说明 | +|---|---|---|---| +| 1 | Convergence | Fig.2 | 收敛曲线对比 | +| 2 | QoE vs SNR | Fig.3 | 不同 SNR 下的系统 QoE | +| 3 | Fairness vs SNR | Fig.4 | 不同 SNR 下的 Jain 公平性 | +| 4 | QoE vs Users | Fig.5 | 用户数量扩展性 | +| 5 | Rate Satisfaction vs Users | Fig.6 | 速率满足度 | +| 6 | Lambda Trajectory | Fig.7-8 | λ(t) 演化轨迹和散点图 | +| 7 | Ablation Study | Fig.10 | 消融实验柱状图 | +| 8 | Sensitivity | Fig.11-12 | β 和 Q_th 敏感性分析 | + +--- + +## 输出文件 / Output Files + +训练和评估产生的文件保存在 `results/` 目录： + +``` +results/ +├── / +│ ├── model_s.pth # 语义智能体模型权重 +│ ├── model_b.pth # 传统智能体模型权重 +│ ├── training_log.json # 训练指标日志 +│ └── config_snapshot.yaml # 训练时的配置快照 +├── figures/ +│ ├── fig02_convergence.png +│ ├── fig03_qoe_vs_snr.png +│ ├── ... +│ └── fig12_qth_sensitivity.png +└── evaluation_results.json # 评估汇总数据 +``` + +--- + +## 已知问题与注意事项 / Known Issues & Notes + +1. **YAML 科学记数法**: 使用 `5.0e+5` 格式（非 `500.0e3`），否则 `yaml.safe_load()` 会将其解析为字符串 +2. **Smoke Test QoE 值**: 2 episode 的 smoke test 中所有算法的 QoE 值相近（~0.7-0.9），这是因为网络尚未充分训练。需完整训练（5000 episodes）才能看到显著差异 +3. **GPU 加速**: 默认自动检测 CUDA。CPU 训练较慢但功能完整 +4. **随机种子**: 默认 seed=42，可在配置中修改 + +--- + +## 论文引用 / Citation + +如引用本工作，请参考论文： + +> Co-MADDPG: 面向语义与传统混合通信的合作竞争多智能体资源分配框架 + +论文文件位于 `../paper/` 目录。 + +--- + +## License + +MIT License diff --git a/code/agents/__init__.py b/code/agents/__init__.py new file mode 100644 index 0000000..2e5e40a --- /dev/null +++ b/code/agents/__init__.py @@ -0,0 +1,6 @@ +"""Agent modules for Co-MADDPG wireless resource allocation.""" + +from .noise import OUNoise +from .replay_buffer import ReplayBuffer + +__all__ = ["OUNoise", "ReplayBuffer"] diff --git a/code/agents/actor.py b/code/agents/actor.py new file mode 100644 index 0000000..f352b60 --- /dev/null +++ b/code/agents/actor.py @@ -0,0 +1,61 @@ +""" +Actor Network for Wireless Resource Allocation / 无线资源分配中的 Actor 网络 + +This file defines the Actor network architecture for the Co-MADDPG project. +The Actor maps local observations to deterministic resource allocation actions. +本文档定义了 Co-MADDPG 项目中的 Actor 网络架构。 +Actor 网络将局部观测值映射到确定性的资源分配动作。 + +Network Architecture / 网络架构: +FC(obs_dim \u2192 256 \u2192 256 \u2192 128 \u2192 act_dim) +Output Mapping / 输出映射: (Tanh + 1) / 2 \u2208 [0, 1] +Reference / 参考文献: Section 3.2.1 Actor-Critic Structure in the project paper. +""" +import torch +import torch.nn as nn + +class Actor(nn.Module): + """ + Actor network for mapping observations to deterministic actions in [0, 1]. + Actor 网络，将观测值映射到 [0, 1] 范围内的确定性动作。 + + Architecture / 架构: FC(obs_dim \u2192 256 \u2192 256 \u2192 128 \u2192 act_dim) + Paper Ref / 论文参考: Section 3.2.1 - Policy Network implementation. + + Args / 参数: + obs_dim (int): Dimension of the observation space. / 观测空间的维度。 + act_dim (int): Dimension of the action space. / 动作空间的维度。 + hidden_sizes (list): Sizes of the three hidden layers (default: [256, 256, 128]). / 三个隐藏层的维度（默认：[256, 256, 128]）。 + """ + def __init__(self, obs_dim, act_dim, hidden_sizes=[256, 256, 128]): + super(Actor, self).__init__() + + # Ensure exactly 3 hidden layers as per model design / 确保按照模型设计包含恰好 3 个隐藏层 + assert len(hidden_sizes) == 3, "Actor requires exactly 3 hidden layer sizes" + + # Define the feedforward neural network / 定义前馈神经网络 + # FC(obs_dim \u2192 256 \u2192 256 \u2192 128 \u2192 act_dim) + self.net = nn.Sequential( + nn.Linear(obs_dim, hidden_sizes[0]), + nn.ReLU(), + nn.Linear(hidden_sizes[0], hidden_sizes[1]), + nn.ReLU(), + nn.Linear(hidden_sizes[1], hidden_sizes[2]), + nn.ReLU(), + nn.Linear(hidden_sizes[2], act_dim) + ) + + def forward(self, obs): + """ + Forward pass for the Actor network. / Actor 网络的前向传播。 + + Args / 参数: + obs (torch.Tensor): Local observation tensor. / 局部观测张量。 + + Returns / 返回: + torch.Tensor: Actions mapped to the [0, 1] range. / 映射到 [0, 1] 范围的动作。 + """ + # Pass observations through the linear layers / 将观测值传入线性层 + out = self.net(obs) + # Formula / 公式: Output (Tanh + 1) / 2 maps result to [0, 1] range / 将输出结果映射到 [0, 1] 范围 + return (torch.tanh(out) + 1.0) / 2.0 diff --git a/code/agents/co_maddpg.py b/code/agents/co_maddpg.py new file mode 100644 index 0000000..08baee6 --- /dev/null +++ b/code/agents/co_maddpg.py @@ -0,0 +1,376 @@ +""" +Co-MADDPG Algorithm for Wireless Resource Allocation / 无线资源分配中的 Co-MADDPG 算法 + +This file implements the Cooperative Multi-Agent Deep Deterministic Policy Gradient (Co-MADDPG) algorithm. +It features a Leader-Follower (Stackelberg) update structure for semantic and traditional agents. +本文档实现了协作式多智能体深度确定性策略梯度 (Co-MADDPG) 算法。 +该算法针对语义智能体和传统智能体采用了领导者-跟随者（Stackelberg）更新结构。 + +Key Components / 关键组件: +- Actor-Critic Architecture / Actor-Critic 架构 +- Stackelberg Update / Stackelberg 更新 (Follower update first, then Leader uses Follower's best response) +- Dynamic Cooperation Weight / 动态协作权重: \u03bb(t) = sigmoid(\u03b2*(QoE_sys - Q_th)) +- Mixed Reward / 混合奖励: r_i = \u03bb*r_coop + (1-\u03bb)*r_comp +- Soft Update / 软更新: \u03b8_target \u2190 \u03c4*\u03b8 + (1-\u03c4)*\u03b8_target + +Reference / 参考文献: Section 3.2 Leader-Follower Game and Co-MADDPG in the project paper. +""" +import os +import torch +import torch.nn as nn +import torch.optim as optim +import numpy as np + +from agents.actor import Actor +from agents.critic import Critic +from agents.replay_buffer import ReplayBuffer +from agents.noise import OUNoise + +class CoMADDPG: + """ + Co-MADDPG Algorithm featuring Leader-Follower updating structure. + 具有领导者-跟随者更新结构的 Co-MADDPG 算法。 + + Agent S: Semantic Agent (Leader) / 语义智能体（领导者） + Agent B: Traditional/Bit-stream Agent (Follower) / 传统/比特流智能体（跟随者） + + Paper Ref / 论文参考: Section 3.2 - Co-MADDPG Implementation details. + """ + def __init__(self, config): + self.config = config + + # Dimensions derived from config / 从配置中提取维度信息 + # Dimensions derived from config + self.obs_dim = config['env']['num_subcarriers'] + 4 + self.act_dim = 3 + + # The critic observes joint states and actions / Critic 观察联合状态和动作 + # The critic observes joint states and actions + obs_dim_total = self.obs_dim * 2 + act_dim_total = self.act_dim * 2 + + # Determine device implicitly / 自动检测设备 (CUDA 或 CPU) + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Hyperparameters / 超参数设置 + # Hyperparameters + train_cfg = config.get('training', {}) + self.gamma = train_cfg.get('gamma', 0.95) + self.tau = train_cfg.get('tau', 0.01) + self.beta = train_cfg.get('beta', 5.0) + self.q_threshold = train_cfg.get('q_threshold', 0.6) + self.batch_size = train_cfg.get('batch_size', 256) + + actor_lr = train_cfg.get('actor_lr', 1e-4) + critic_lr = train_cfg.get('critic_lr', 3e-4) + buffer_capacity = train_cfg.get('buffer_capacity', 100000) + + # Network configurations / 网络配置项 + # Network configurations + net_cfg = config.get('network', {}) + actor_hidden = net_cfg.get('actor_hidden', [256, 256, 128]) + critic_hidden = net_cfg.get('critic_hidden', [512, 512, 256]) + + # Create Actor Networks / 创建 Actor 网络 + self.actor_s = Actor(self.obs_dim, self.act_dim, actor_hidden).to(self.device) + self.actor_b = Actor(self.obs_dim, self.act_dim, actor_hidden).to(self.device) + + # Create Actor Target Networks / 创建 Actor 目标网络 + self.actor_s_target = Actor(self.obs_dim, self.act_dim, actor_hidden).to(self.device) + self.actor_b_target = Actor(self.obs_dim, self.act_dim, actor_hidden).to(self.device) + self.actor_s_target.load_state_dict(self.actor_s.state_dict()) + self.actor_b_target.load_state_dict(self.actor_b.state_dict()) + + # Create Critic Networks / 创建 Critic 网络 + self.critic_s = Critic(obs_dim_total, act_dim_total, critic_hidden).to(self.device) + self.critic_b = Critic(obs_dim_total, act_dim_total, critic_hidden).to(self.device) + + # Create Critic Target Networks / 创建 Critic 目标网络 + self.critic_s_target = Critic(obs_dim_total, act_dim_total, critic_hidden).to(self.device) + self.critic_b_target = Critic(obs_dim_total, act_dim_total, critic_hidden).to(self.device) + self.critic_s_target.load_state_dict(self.critic_s.state_dict()) + self.critic_b_target.load_state_dict(self.critic_b.state_dict()) + + # Optimizers / 优化器设置 + self.actor_optimizer_s = optim.Adam(self.actor_s.parameters(), lr=actor_lr) + self.actor_optimizer_b = optim.Adam(self.actor_b.parameters(), lr=actor_lr) + self.critic_optimizer_s = optim.Adam(self.critic_s.parameters(), lr=critic_lr) + self.critic_optimizer_b = optim.Adam(self.critic_b.parameters(), lr=critic_lr) + + # MSE Loss for critics / Critic 使用的均方误差损失函数 + self.critic_loss_fn = nn.MSELoss() + + # Replay Buffer / 经验回放池 + self.replay_buffer = ReplayBuffer(buffer_capacity) + + # Ornstein-Uhlenbeck noise / OU 探索噪声 + ou_sigma = train_cfg.get('ou_sigma_init', 0.2) + ou_theta = train_cfg.get('ou_theta', 0.15) + self.noise_s = OUNoise(self.act_dim, theta=ou_theta, sigma_init=ou_sigma) + self.noise_b = OUNoise(self.act_dim, theta=ou_theta, sigma_init=ou_sigma) + + def select_action(self, obs_s, obs_b, explore=True): + """ + Determines the actions using the actor networks, with optional OU exploration noise. + 使用 Actor 网络确定动作，可选择性添加 OU 探索噪声。 + + Args / 参数: + obs_s, obs_b: Observations for agents S and B. / 智能体 S 和 B 的观测值。 + explore (bool): Whether to add noise for exploration. / 是否添加探索噪声。 + + Returns / 返回: + tuple: (act_s, act_b) actions for each agent. / 每个智能体的动作 (act_s, act_b)。 + """ + self.actor_s.eval() + self.actor_b.eval() + + with torch.no_grad(): + obs_s_t = torch.FloatTensor(obs_s).unsqueeze(0).to(self.device) + obs_b_t = torch.FloatTensor(obs_b).unsqueeze(0).to(self.device) + + act_s = self.actor_s(obs_s_t).cpu().numpy().squeeze(0) + act_b = self.actor_b(obs_b_t).cpu().numpy().squeeze(0) + + self.actor_s.train() + self.actor_b.train() + + # Apply OU noise if exploration is enabled / 如果开启探索，则添加 OU 噪声 + if explore: + act_s += self.noise_s.sample() + act_b += self.noise_b.sample() + + # Formula / 公式: act \u2208 [0, 1] + # Clip mapping bounds as enforced by the (tanh + 1)/2 activation in Actor / 按照 Actor 中的激活函数限制动作范围到 [0, 1] + act_s = np.clip(act_s, 0.0, 1.0) + act_b = np.clip(act_b, 0.0, 1.0) + + return act_s, act_b + + def compute_lambda(self, qoe_sys): + """ + Compute dynamic cooperation weight \u03bb(t). / 计算动态协作权重 \u03bb(t)。 + + Formula / 公式: \u03bb(t) = sigmoid(\u03b2 * (QoE_sys - Q_th)) + + Args / 参数: + qoe_sys (float): Current system QoE. / 当前系统 QoE。 + + Returns / 返回: + float: Cooperation weight \u03bb(t) \u2208 [0, 1]. / 协作权重 \u03bb(t)。 + """ + return 1.0 / (1.0 + np.exp(-self.beta * (qoe_sys - self.q_threshold))) + + def compute_rewards(self, qoe_s, qoe_b, qoe_sys): + """ + Compute joint dynamically weighted rewards based on \u03bb cooperation factor. + 基于 \u03bb 协作因子计算动态加权的联合奖励。 + + Formula / 公式: r_i = \u03bb * r_coop_i + (1 - \u03bb) * r_comp_i + + Args / 参数: + qoe_s, qoe_b, qoe_sys: QoE values for semantic, traditional, and system levels. / 语义层、传统层和系统层的 QoE 值。 + + Returns / 返回: + tuple: (r_s, r_b, lambda_val) final mixed rewards and the cooperation weight. / 最终混合奖励与协作权重。 + """ + lambda_val = self.compute_lambda(qoe_sys) + + rew_cfg = self.config.get('reward', {}) + coop_self = rew_cfg.get('coop_self', 0.5) + coop_other = rew_cfg.get('coop_other', 0.3) + coop_sys = rew_cfg.get('coop_sys', 0.2) + + comp_self = rew_cfg.get('comp_self', 0.8) + comp_sys = rew_cfg.get('comp_sys', 0.2) + + # Cooperative logic (shared benefit mindset) / 协作逻辑（共同利益导向） + # Formula / 公式: r_coop_i = 0.5*qoe_i + 0.3*qoe_j + 0.2*qoe_sys + r_coop_s = coop_self * qoe_s + coop_other * qoe_b + coop_sys * qoe_sys + r_coop_b = coop_self * qoe_b + coop_other * qoe_s + coop_sys * qoe_sys + + # Competitive logic (individual maximization mindset) / 竞争逻辑（个体利益导向） + # Formula / 公式: r_comp_i = 0.8*qoe_i + 0.2*qoe_sys + r_comp_s = comp_self * qoe_s + comp_sys * qoe_sys + r_comp_b = comp_self * qoe_b + comp_sys * qoe_sys + + # Dynamically balanced reward (mix based on System QoE state vs threshold) / 动态平衡奖励（基于系统 QoE 状态与阈值的混合） + r_s = lambda_val * r_coop_s + (1.0 - lambda_val) * r_comp_s + r_b = lambda_val * r_coop_b + (1.0 - lambda_val) * r_comp_b + + return r_s, r_b, lambda_val + + def update(self): + """ + Perform a gradient update loop containing Leader-Follower sequential methodology. + 执行包含领导者-跟随者顺序方法的梯度更新循环。 + + Update Order / 更新顺序: + 1. Update Follower (Agent B) Critic & Actor / 更新跟随者（智能体 B）的 Critic 和 Actor + 2. Update Leader (Agent S) Critic & Actor / 更新领导者（智能体 S）的 Critic 和 Actor + + Returns / 返回: + tuple: (critic_loss_s, critic_loss_b, actor_loss_s, actor_loss_b) or None if buffer not ready. / 各项损失值。 + """ + if len(self.replay_buffer) < self.batch_size: + return None + + # Sample batch from replay buffer / 从回放池中采样批次数据 + batch = self.replay_buffer.sample(self.batch_size) + + # Destructure standardized tuple. Assumes order: + # (obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, dones) + obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, dones = batch + + obs_s = torch.FloatTensor(obs_s).to(self.device) + obs_b = torch.FloatTensor(obs_b).to(self.device) + act_s = torch.FloatTensor(act_s).to(self.device) + act_b = torch.FloatTensor(act_b).to(self.device) + rew_s = torch.FloatTensor(rew_s).unsqueeze(1).to(self.device) + rew_b = torch.FloatTensor(rew_b).unsqueeze(1).to(self.device) + next_obs_s = torch.FloatTensor(next_obs_s).to(self.device) + next_obs_b = torch.FloatTensor(next_obs_b).to(self.device) + dones = torch.FloatTensor(dones).unsqueeze(1).to(self.device) + + # Construct joint states & actions for centralized critic / 构建用于集中式 Critic 的联合状态和动作空间 + obs_all = torch.cat([obs_s, obs_b], dim=1) + next_obs_all = torch.cat([next_obs_s, next_obs_b], dim=1) + act_all = torch.cat([act_s, act_b], dim=1) + + # Target actions for next state / 计算下一状态的目标动作值 + with torch.no_grad(): + next_act_s_target = self.actor_s_target(next_obs_s) + next_act_b_target = self.actor_b_target(next_obs_b) + next_act_all_target = torch.cat([next_act_s_target, next_act_b_target], dim=1) + + # ===================================================================== + # PHASE 1: Update Follower (Agent B) FIRST / 第一阶段：首先更新跟随者 (智能体 B) + # Stackelberg methodology / Stackelberg 方法论: Follower responds to Leader's action / 跟随者响应领导者的动作 + # ===================================================================== + # PHASE 1: Update Follower (Agent B) FIRST + # ===================================================================== + + # Update Critic B / 更新智能体 B 的 Critic + with torch.no_grad(): + target_q_b_next = self.critic_b_target(next_obs_all, next_act_all_target) + target_q_b = rew_b + self.gamma * (1.0 - dones) * target_q_b_next + + current_q_b = self.critic_b(obs_all, act_all) + critic_loss_b = self.critic_loss_fn(current_q_b, target_q_b) + + self.critic_optimizer_b.zero_grad() + critic_loss_b.backward() + self.critic_optimizer_b.step() + + # Update Actor B / 更新智能体 B 的 Actor + # Loss: -mean(critic_b(obs_all, [act_s_from_buffer, actor_b(obs_b)])) + # In Phase 1, the follower assumes leader's action from replay buffer / 在第一阶段，跟随者假定领导者的动作为回放池中的动作 + # Loss: -mean(critic_b(obs_all, [act_s_from_buffer, actor_b(obs_b)])) + new_act_b = self.actor_b(obs_b) + act_all_for_b = torch.cat([act_s, new_act_b], dim=1) + + actor_loss_b = -self.critic_b(obs_all, act_all_for_b).mean() + + self.actor_optimizer_b.zero_grad() + actor_loss_b.backward() + self.actor_optimizer_b.step() + + # ===================================================================== + # PHASE 2: Update Leader (Agent S) with UPDATED Follower / 第二阶段：基于更新后的跟随者更新领导者 (智能体 S) + # Leader S uses Follower B's best response / 领导者 S 利用跟随者 B 的最佳响应函数 + # ===================================================================== + # PHASE 2: Update Leader (Agent S) with UPDATED Follower + # ===================================================================== + + # Update Critic S / 更新智能体 S 的 Critic + with torch.no_grad(): + target_q_s_next = self.critic_s_target(next_obs_all, next_act_all_target) + target_q_s = rew_s + self.gamma * (1.0 - dones) * target_q_s_next + + current_q_s = self.critic_s(obs_all, act_all) + critic_loss_s = self.critic_loss_fn(current_q_s, target_q_s) + + self.critic_optimizer_s.zero_grad() + critic_loss_s.backward() + self.critic_optimizer_s.step() + + # Update Actor S / 更新智能体 S 的 Actor + # KEY / 核心逻辑: Use newly updated actor_b(obs_b).detach() as follower's assumed action / 使用刚更新的 actor_b(obs_b).detach() 作为跟随者的预估动作 + # This represents the Leader's knowledge of the Follower's best response / 这代表了领导者对跟随者最佳响应的认知 + # KEY: Use newly updated actor_b(obs_b).detach() as follower's assumed action + new_act_s = self.actor_s(obs_s) + updated_act_b_detached = self.actor_b(obs_b).detach() + act_all_for_s = torch.cat([new_act_s, updated_act_b_detached], dim=1) + + actor_loss_s = -self.critic_s(obs_all, act_all_for_s).mean() + + self.actor_optimizer_s.zero_grad() + actor_loss_s.backward() + self.actor_optimizer_s.step() + + # ===================================================================== + # Target Networks Soft Update / 目标网络软更新 + # Formula / 公式: \u03b8_target \u2190 \u03c4 * \u03b8 + (1 - \u03c4) * \u03b8_target + # ===================================================================== + # Target Networks Soft Update + # ===================================================================== + self.soft_update(self.actor_s_target, self.actor_s, self.tau) + self.soft_update(self.actor_b_target, self.actor_b, self.tau) + self.soft_update(self.critic_s_target, self.critic_s, self.tau) + self.soft_update(self.critic_b_target, self.critic_b, self.tau) + + return critic_loss_s.item(), critic_loss_b.item(), actor_loss_s.item(), actor_loss_b.item() + + def soft_update(self, target, source, tau): + """ + Polyak averaging for target network parameters. / 目标网络参数的 Polyak 平均（软更新）。 + + Args / 参数: + target: Target network. / 目标网络。 + source: Source network. / 源网络。 + tau (float): Soft update interpolation factor. / 软更新插值因子 \u03c4。 + """ + for target_param, source_param in zip(target.parameters(), source.parameters()): + target_param.data.copy_(tau * source_param.data + (1.0 - tau) * target_param.data) + + def save(self, path): + """ + Saves all 4 network state_dicts and optimizers. / 保存所有 4 个网络的权重和优化器状态。 + + Args / 参数: + path (str): File path to save the checkpoint. / 保存检查点的文件路径。 + """ + os.makedirs(os.path.dirname(path), exist_ok=True) + torch.save({ + 'actor_s': self.actor_s.state_dict(), + 'actor_b': self.actor_b.state_dict(), + 'critic_s': self.critic_s.state_dict(), + 'critic_b': self.critic_b.state_dict(), + 'actor_optimizer_s': self.actor_optimizer_s.state_dict(), + 'actor_optimizer_b': self.actor_optimizer_b.state_dict(), + 'critic_optimizer_s': self.critic_optimizer_s.state_dict(), + 'critic_optimizer_b': self.critic_optimizer_b.state_dict(), + }, path) + + def load(self, path): + """ + Loads all 4 networks and optimizer parameters from saved states. / 从保存的状态加载所有 4 个网络和优化器参数。 + + Args / 参数: + path (str): File path of the checkpoint to load. / 要加载的检查点文件路径。 + """ + checkpoint = torch.load(path, map_location=self.device) + self.actor_s.load_state_dict(checkpoint['actor_s']) + self.actor_b.load_state_dict(checkpoint['actor_b']) + self.critic_s.load_state_dict(checkpoint['critic_s']) + self.critic_b.load_state_dict(checkpoint['critic_b']) + + self.actor_optimizer_s.load_state_dict(checkpoint['actor_optimizer_s']) + self.actor_optimizer_b.load_state_dict(checkpoint['actor_optimizer_b']) + self.critic_optimizer_s.load_state_dict(checkpoint['critic_optimizer_s']) + self.critic_optimizer_b.load_state_dict(checkpoint['critic_optimizer_b']) + + # Hard sync the target networks after loading + self.actor_s_target.load_state_dict(self.actor_s.state_dict()) + self.actor_b_target.load_state_dict(self.actor_b.state_dict()) + self.critic_s_target.load_state_dict(self.critic_s.state_dict()) + self.critic_b_target.load_state_dict(self.critic_b.state_dict()) diff --git a/code/agents/critic.py b/code/agents/critic.py new file mode 100644 index 0000000..d4f36aa --- /dev/null +++ b/code/agents/critic.py @@ -0,0 +1,63 @@ +""" +Critic Network for Wireless Resource Allocation / 无线资源分配中的 Critic 网络 + +This file defines the Critic network architecture for the Co-MADDPG project. +The Critic estimates the joint Q-value based on the global observations and actions of all agents. +本文档定义了 Co-MADDPG 项目中的 Critic 网络架构。 +Critic 网络基于所有智能体的全局观测和动作来估算联合 Q 值。 + +Network Architecture / 网络架构: +FC(obs_dim_total + act_dim_total \u2192 512 \u2192 512 \u2192 256 \u2192 1) +Input / 输入: Concatenated observations and actions / 拼接后的观测与动作 +Reference / 参考文献: Section 3.2.1 Actor-Critic Structure in the project paper. +""" +import torch +import torch.nn as nn + +class Critic(nn.Module): + """ + Critic network for assessing the value of joint actions given joint observations. + Critic 网络，用于在给定联合观测的情况下评估联合动作的价值。 + + Architecture / 架构: FC(obs_dim_total + act_dim_total \u2192 512 \u2192 512 \u2192 256 \u2192 1) + Paper Ref / 论文参考: Section 3.2.1 - Centralized Critic implementation. + + Args / 参数: + obs_dim_total (int): Total dimension of concatenated observations. / 所有智能体拼接后的总观测维度。 + act_dim_total (int): Total dimension of concatenated actions. / 所有智能体拼接后的总动作维度。 + hidden_sizes (list): Sizes of the three hidden layers (default: [512, 512, 256]). / 三个隐藏层的维度（默认：[512, 512, 256]）。 + """ + def __init__(self, obs_dim_total, act_dim_total, hidden_sizes=[512, 512, 256]): + super(Critic, self).__init__() + + # Ensure exactly 3 hidden layers as per model design / 确保按照模型设计包含恰好 3 个隐藏层 + assert len(hidden_sizes) == 3, "Critic requires exactly 3 hidden layer sizes" + + # Define the feedforward neural network / 定义前馈神经网络 + # FC(obs_dim_total + act_dim_total \u2192 512 \u2192 512 \u2192 256 \u2192 1) + self.net = nn.Sequential( + nn.Linear(obs_dim_total + act_dim_total, hidden_sizes[0]), + nn.ReLU(), + nn.Linear(hidden_sizes[0], hidden_sizes[1]), + nn.ReLU(), + nn.Linear(hidden_sizes[1], hidden_sizes[2]), + nn.ReLU(), + nn.Linear(hidden_sizes[2], 1) + ) + + def forward(self, obs_all, act_all): + """ + Forward pass for the Critic network. / Critic 网络的前向传播。 + + Args / 参数: + obs_all (torch.Tensor): The concatenated joint observation tensor. / 拼接后的联合观测张量。 + act_all (torch.Tensor): The concatenated joint action tensor. / 拼接后的联合动作张量。 + + Returns / 返回: + torch.Tensor: Scalar Q-value evaluation. / 标量 Q 值评估结果。 + """ + # Formula / 公式: x = [obs_total, act_total] + # Concatenate joint states and actions together for input / 将联合状态和动作拼接作为输入 + x = torch.cat([obs_all, act_all], dim=1) + # Pass the concatenated input through the network / 将拼接后的输入传入网络 + return self.net(x) diff --git a/code/agents/noise.py b/code/agents/noise.py new file mode 100644 index 0000000..08de732 --- /dev/null +++ b/code/agents/noise.py @@ -0,0 +1,74 @@ +""" +Ornstein-Uhlenbeck (OU) Exploration Noise / OU 探索噪声 + +This file implements the OU noise process for continuous action exploration. +The noise is temporally correlated and features linear sigma decay over training. +本文档实现了用于连续动作探索的 OU 噪声过程。 +该噪声具有时间相关性，并在训练过程中具有线性标准差（sigma）衰减特性。 + +Formula / 公式: dx = \u03b8(\u03bc - x)dt + \u03c3dW +Decay / 衰减: Linear sigma decay over specified decay period. / 在指定的衰减周期内线性衰减 sigma。 +Reference / 参考文献: Section 3.2.2 Exploration Mechanism in the project paper. +""" +import numpy as np + + +class OUNoise: + """Ornstein-Uhlenbeck process for temporally correlated exploration noise. + 用于生成具有时间相关性的探索噪声的 Ornstein-Uhlenbeck 过程。 + + Formula / 公式: dx = \u03b8(\u03bc - x)dt + \u03c3dW + + Args / 参数: + action_dim (int): Dimensionality of the action space. / 动作空间的维度。 + mu (float): Long-term mean of the process. / 过程的长期均值 \u03bc。 + theta (float): Mean-reversion rate. / 均值回归速率 \u03b8。 + sigma_init (float): Initial standard deviation. / 初始标准差 \u03c3。 + sigma_min (float): Minimum standard deviation after decay. / 衰减后的最小标准差。 + decay_period (int): Number of episodes over which sigma decays linearly. / sigma 线性衰减的总回合数。 + """ + + def __init__(self, action_dim: int, mu: float = 0.0, theta: float = 0.15, + sigma_init: float = 0.2, sigma_min: float = 0.01, + decay_period: int = 5000): + # Initialize noise parameters and state / 初始化噪声参数和状态 + self.action_dim = action_dim + self.mu = mu + self.theta = theta + self.sigma_init = sigma_init + self.sigma_min = sigma_min + self.sigma = sigma_init + self.decay_period = decay_period + self.state = np.full(action_dim, mu, dtype=np.float64) + + def reset(self): + """ + Reset the internal state to the mean. / 将内部状态重置为均值 \u03bc。 + """ + self.state = np.full(self.action_dim, self.mu, dtype=np.float64) + + def decay_sigma(self, episode: int): + """Linearly decay sigma from sigma_init to sigma_min over decay_period. + 在衰减周期内，将 sigma 从初始值线性衰减到最小值。 + + Args / 参数: + episode (int): Current episode number. / 当前回合数。 + """ + # Calculate decay fraction / 计算衰减比例 + frac = min(1.0, episode / max(1, self.decay_period)) + # Linear decay formula / 线性衰减公式: \u03c3 = \u03c3_init + frac * (\u03c3_min - \u03c3_init) + self.sigma = self.sigma_init + frac * (self.sigma_min - self.sigma_init) + + def sample(self) -> np.ndarray: + """ + Generate a noise sample via the OU process. / 通过 OU 过程生成噪声样本。 + + Returns / 返回: + np.ndarray: Noise vector of shape (action_dim,). / 形状为 (action_dim,) 的噪声向量。 + """ + # OU Formula / OU 公式: dx = \u03b8 * (\u03bc - x) + \u03c3 * N(0,1) + dx = (self.theta * (self.mu - self.state) + + self.sigma * np.random.randn(self.action_dim)) + # Update state / 更新状态: x = x + dx + self.state = self.state + dx + return self.state.copy() diff --git a/code/agents/replay_buffer.py b/code/agents/replay_buffer.py new file mode 100644 index 0000000..3b68004 --- /dev/null +++ b/code/agents/replay_buffer.py @@ -0,0 +1,92 @@ +""" +Experience Replay Buffer for Multi-Agent RL / 多智能体强化学习的经验回放池 + +This file implements a fixed-size replay buffer to store and sample transitions. +Each transition contains observations, actions, and rewards for both semantic and traditional agents. +本文档实现了一个固定大小的回放池，用于存储和采样状态转移。 +每个状态转移包含语义智能体和传统智能体的观测、动作及奖励。 + +Storage Format / 存储格式: 9-field transitions / 9 字段状态转移 +(obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, done) +Reference / 参考文献: Section 3.2.3 Experience Replay in the project paper. +""" +import random +from collections import deque + +import numpy as np + + +class ReplayBuffer: + """Fixed-size experience replay buffer for two-agent transitions. + 用于双智能体状态转移的固定大小经验回放池。 + + Stores transitions of the form / 存储如下形式的状态转移: + (obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, done) + + Args / 参数: + capacity (int): Maximum number of transitions to store. / 存储转换的最大数量。 + """ + + def __init__(self, capacity: int): + # Initialize the buffer as a double-ended queue with a maximum length / 将回放池初始化为具有最大长度的双端队列 + self.buffer = deque(maxlen=capacity) + + def push(self, obs_s, obs_b, act_s, act_b, rew_s, rew_b, + next_obs_s, next_obs_b, done=False): + """ + Store a single transition into the buffer. / 将单次状态转移存入回放池。 + + Args / 参数: + obs_s, obs_b: Observations for Semantic and Traditional agents. / 语义智能体与传统智能体的观测。 + act_s, act_b: Actions taken by each agent. / 各个智能体采取的动作。 + rew_s, rew_b: Rewards received by each agent. / 各个智能体获得的奖励。 + next_obs_s, next_obs_b: Next observations. / 下一个状态的观测。 + done (bool): Whether the episode ended. / 回合是否结束。 + """ + # Append the 9-field transition to the deque / 将 9 字段的状态转移添加到队列中 + self.buffer.append(( + np.asarray(obs_s, dtype=np.float32), + np.asarray(obs_b, dtype=np.float32), + np.asarray(act_s, dtype=np.float32), + np.asarray(act_b, dtype=np.float32), + float(rew_s), + float(rew_b), + np.asarray(next_obs_s, dtype=np.float32), + np.asarray(next_obs_b, dtype=np.float32), + float(done), + )) + + def sample(self, batch_size: int): + """ + Sample a random batch of transitions for training. / 随机采样一批状态转移用于训练。 + + Args / 参数: + batch_size (int): Number of transitions to sample. / 采样数量。 + + Returns / 返回: + tuple of np.ndarray: (obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, dones) + Each array has shape (batch_size, ...). / 每个数组的形状均为 (batch_size, ...)。 + """ + # Randomly select 'batch_size' samples from the buffer / 从回放池中随机选择 batch_size 个样本 + batch = random.sample(self.buffer, batch_size) + # Unzip the batch into separate components / 将采样到的批次拆解为独立的组件 + obs_s, obs_b, act_s, act_b, rew_s, rew_b, \ + next_obs_s, next_obs_b, dones = zip(*batch) + # Convert each component to a numpy array / 将各组件转换为 numpy 数组 + return ( + np.array(obs_s), + np.array(obs_b), + np.array(act_s), + np.array(act_b), + np.array(rew_s), + np.array(rew_b), + np.array(next_obs_s), + np.array(next_obs_b), + np.array(dones), + ) + + def __len__(self) -> int: + """ + Return the current size of the buffer. / 返回回放池的当前大小。 + """ + return len(self.buffer) diff --git a/code/baselines/__init__.py b/code/baselines/__init__.py new file mode 100644 index 0000000..21a8b7f --- /dev/null +++ b/code/baselines/__init__.py @@ -0,0 +1,12 @@ +from .pure_coop import PureCooperative +from .pure_comp import PureCompetitive +from .single_dqn import SingleAgentDQN +from .iddpg import IndependentDDPG +from .fixed_lambda import FixedLambda +from .equal_alloc import EqualAllocation +from .semantic_only import SemanticOnly + +__all__ = [ + "PureCooperative", "PureCompetitive", "SingleAgentDQN", + "IndependentDDPG", "FixedLambda", "EqualAllocation", "SemanticOnly", +] diff --git a/code/baselines/equal_alloc.py b/code/baselines/equal_alloc.py new file mode 100644 index 0000000..d6e3a74 --- /dev/null +++ b/code/baselines/equal_alloc.py @@ -0,0 +1,101 @@ +import numpy as np + +""" +Baseline: EqualAllocation (等额分配基线) +===================================== +Purpose (lower bound): +- This baseline represents a simple heuristic approach with no learning involved. +- It serves as a lower bound for performance comparison, showing the system behavior under a naive, fixed resource allocation strategy. +- 目的（性能下限）：该基线代表了一种不涉及学习的简单启发式方法。它作为性能对比的下限，展示了在朴素的固定资源分配策略下系统的表现。 + +Difference from Co-MADDPG: +1. Learning: No learning vs Deep Reinforcement Learning. +2. Action Selection: Always fixed at [0.5, 0.5, 0.5] for all resource parameters (subcarrier fraction, power, m_param). +3. 与 Co-MADDPG 的区别： + - 学习机制：无学习 vs 深度强化学习。 + - 动作选择：所有资源参数（子载波比例、功率、m 参数）始终固定为 [0.5, 0.5, 0.5]。 + +Contribution: +- Contributes to performance baseline tables as the "Random/Fixed" comparison point. +- 贡献：作为“随机/固定”对比点，用于性能基准表。 +""" + +class DummyBuffer: + """ + Dummy replay buffer that satisfies train.py's push/len interface. + 满足 train.py 中 push/len 接口要求的虚拟重放池。 + """ + def push(self, *args): + # Do nothing as no learning is performed + # 不执行任何操作，因为没有学习过程 + pass + + def __len__(self): + # Always return 0 to indicate no samples available + # 始终返回 0，表示没有可用样本 + return 0 + + +class EqualAllocation: + """ + EqualAllocation algorithm implementation. + 等额分配算法实现。 + """ + def __init__(self, config): + # Initialize with configuration and a dummy buffer + # 使用配置和虚拟重放池进行初始化 + self.config = config + self.replay_buffer = DummyBuffer() + + def select_action(self, obs_s, obs_b, explore=True): + """ + Always return a fixed action [0.5, 0.5, 0.5]. + 始终返回固定动作 [0.5, 0.5, 0.5]。 + """ + return np.array([0.5, 0.5, 0.5], dtype=np.float32), \ + np.array([0.5, 0.5, 0.5], dtype=np.float32) + + def compute_rewards(self, qoe_s, qoe_b, qoe_sys): + """ + Compute rewards using a fixed λ=0.5 for consistency in monitoring. + 使用固定 λ=0.5 计算奖励，以保持监测的一致性。 + + Formula: Balanced combination of coop and comp components. + 公式说明：协作项与竞争项的平衡组合。 + """ + lam = 0.5 + rew_cfg = self.config.get('reward', {}) + coop_self = rew_cfg.get('coop_self', 0.5) + coop_other = rew_cfg.get('coop_other', 0.3) + coop_sys = rew_cfg.get('coop_sys', 0.2) + comp_self = rew_cfg.get('comp_self', 0.8) + comp_sys = rew_cfg.get('comp_sys', 0.2) + + # Compute reward components for S + # 计算 S 的奖励组成部分 + r_coop_s = coop_self * qoe_s + coop_other * qoe_b + coop_sys * qoe_sys + r_comp_s = comp_self * qoe_s + comp_sys * qoe_sys + r_s = lam * r_coop_s + (1 - lam) * r_comp_s + + # Compute reward components for B + # 计算 B 的奖励组成部分 + r_coop_b = coop_self * qoe_b + coop_other * qoe_s + coop_sys * qoe_sys + r_comp_b = comp_self * qoe_b + comp_sys * qoe_sys + r_b = lam * r_coop_b + (1 - lam) * r_comp_b + + return r_s, r_b, lam + + def update(self): + """ + No update performed in heuristic baseline. + 启发式基线中不执行更新。 + """ + return None + + def save(self, path): + """No state to save.""" + pass + + def load(self, path): + """No state to load.""" + pass diff --git a/code/baselines/fixed_lambda.py b/code/baselines/fixed_lambda.py new file mode 100644 index 0000000..ddbd470 --- /dev/null +++ b/code/baselines/fixed_lambda.py @@ -0,0 +1,280 @@ +import os +import numpy as np +import torch +import torch.nn.functional as F + +from agents.actor import Actor +from agents.critic import Critic +from agents.replay_buffer import ReplayBuffer +from agents.noise import OUNoise + +""" +Baseline: FixedLambda (固定 λ 基线) +===================================== +Purpose (ablation): +- This baseline is used to evaluate the benefit of the dynamic lambda switching mechanism in Co-MADDPG. +- It fixes λ at a constant value (0.5), balancing cooperation and competition equally throughout the training. +- 目的（消融实验）：该基线用于评估 Co-MADDPG 中动态 λ 切换机制的收益。它将 λ 固定为常数（0.5），在整个训练过程中平衡协作与竞争。 + +Difference from Co-MADDPG: +1. Lambda (λ): Fixed at 0.5, whereas Co-MADDPG dynamically adjusts λ based on system state. +2. Update Order: Retains the Stackelberg update order (follower B first, then leader S), same as Co-MADDPG. +3. 与 Co-MADDPG 的区别： + - Lambda (λ): 固定为 0.5，而 Co-MADDPG 根据系统状态动态调整 λ。 + - 更新顺序：保留了 Stackelberg 博弈更新顺序（先更新从属者 B，再更新主导者 S），与 Co-MADDPG 一致。 + +Contribution: +- Contributes to performance sensitivity analysis regarding the choice of λ and shows why a fixed balance is suboptimal. +- 贡献：用于关于 λ 选择的性能敏感性分析，展示为什么固定比例的平衡并非最优。 +""" + +class FixedLambda: + """ + FixedLambda algorithm implementation. + 固定 λ 算法实现。 + """ + def __init__(self, config): + # Initialize configuration and device + # 初始化配置和设备 + self.config = config + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Hyperparameters: Gamma, Tau, Batch Size, and Fixed λ=0.5 + # 超参数：折扣因子、软更新系数、批量大小以及固定 λ=0.5 + self.gamma = config['training']['gamma'] + self.tau = config['training']['tau'] + self.batch_size = config['training']['batch_size'] + self.fixed_lambda = 0.5 + + # Dimensions: State and Action + # 维度信息：状态与动作 + self.obs_dim = config['env']['num_subcarriers'] + 4 + self.act_dim = 3 + + # Actor networks and their target networks + # Actor 网络及其目标网络 + hidden_a = config['network']['actor_hidden'] + hidden_c = config['network']['critic_hidden'] + + self.actor_s = Actor(self.obs_dim, self.act_dim, hidden_a).to(self.device) + self.actor_b = Actor(self.obs_dim, self.act_dim, hidden_a).to(self.device) + self.actor_s_target = Actor(self.obs_dim, self.act_dim, hidden_a).to(self.device) + self.actor_b_target = Actor(self.obs_dim, self.act_dim, hidden_a).to(self.device) + self.actor_s_target.load_state_dict(self.actor_s.state_dict()) + self.actor_b_target.load_state_dict(self.actor_b.state_dict()) + + # Joint Critics for Centralized Training + # 用于中心化训练的联合 Critic + obs_total = self.obs_dim * 2 + act_total = self.act_dim * 2 + self.critic_s = Critic(obs_total, act_total, hidden_c).to(self.device) + self.critic_b = Critic(obs_total, act_total, hidden_c).to(self.device) + self.critic_s_target = Critic(obs_total, act_total, hidden_c).to(self.device) + self.critic_b_target = Critic(obs_total, act_total, hidden_c).to(self.device) + self.critic_s_target.load_state_dict(self.critic_s.state_dict()) + self.critic_b_target.load_state_dict(self.critic_b.state_dict()) + + # Optimizers for actors and critics + # Actor 与 Critic 的优化器 + self.actor_s_optimizer = torch.optim.Adam(self.actor_s.parameters(), lr=config['training']['actor_lr']) + self.actor_b_optimizer = torch.optim.Adam(self.actor_b.parameters(), lr=config['training']['actor_lr']) + self.critic_s_optimizer = torch.optim.Adam(self.critic_s.parameters(), lr=config['training']['critic_lr']) + self.critic_b_optimizer = torch.optim.Adam(self.critic_b.parameters(), lr=config['training']['critic_lr']) + + # Experience Replay and OU Noise for exploration + # 经验重放池与用于探索的 OU 噪声 + self.replay_buffer = ReplayBuffer(config['training']['buffer_capacity']) + self.noise_s = OUNoise(self.act_dim, theta=config['training']['ou_theta'], + sigma_init=config['training']['ou_sigma_init'], + sigma_min=config['training']['ou_sigma_min']) + self.noise_b = OUNoise(self.act_dim, theta=config['training']['ou_theta'], + sigma_init=config['training']['ou_sigma_init'], + sigma_min=config['training']['ou_sigma_min']) + + def select_action(self, obs_s, obs_b, explore=True): + """ + Select actions for both agents given observations. + 根据观察结果为两个智能体选择动作。 + """ + self.actor_s.eval() + self.actor_b.eval() + with torch.no_grad(): + obs_s_t = torch.FloatTensor(obs_s).unsqueeze(0).to(self.device) + obs_b_t = torch.FloatTensor(obs_b).unsqueeze(0).to(self.device) + act_s = self.actor_s(obs_s_t).cpu().numpy()[0] + act_b = self.actor_b(obs_b_t).cpu().numpy()[0] + self.actor_s.train() + self.actor_b.train() + + if explore: + # Add noise during training exploration + # 训练探索期间增加噪声 + act_s = np.clip(act_s + self.noise_s.sample(), 0.0, 1.0) + act_b = np.clip(act_b + self.noise_b.sample(), 0.0, 1.0) + else: + act_s = np.clip(act_s, 0.0, 1.0) + act_b = np.clip(act_b, 0.0, 1.0) + + return act_s, act_b + + def compute_rewards(self, qoe_s, qoe_b, qoe_sys): + """ + Compute rewards with fixed λ=0.5. + 使用固定 λ=0.5 计算奖励。 + + Formula: r_i = 0.5 * r_coop + 0.5 * r_comp + 公式说明：奖励是协作项与竞争项的等权之和。 + """ + lam = self.fixed_lambda + rew_cfg = self.config.get('reward', {}) + coop_self = rew_cfg.get('coop_self', 0.5) + coop_other = rew_cfg.get('coop_other', 0.3) + coop_sys = rew_cfg.get('coop_sys', 0.2) + comp_self = rew_cfg.get('comp_self', 0.8) + comp_sys = rew_cfg.get('comp_sys', 0.2) + + # Compute Cooperative and Competitive components for S + # 计算 S 的协作与竞争组成部分 + r_coop_s = coop_self * qoe_s + coop_other * qoe_b + coop_sys * qoe_sys + r_comp_s = comp_self * qoe_s + comp_sys * qoe_sys + r_s = lam * r_coop_s + (1 - lam) * r_comp_s + + # Compute Cooperative and Competitive components for B + # 计算 B 的协作与竞争组成部分 + r_coop_b = coop_self * qoe_b + coop_other * qoe_s + coop_sys * qoe_sys + r_comp_b = comp_self * qoe_b + comp_sys * qoe_sys + r_b = lam * r_coop_b + (1 - lam) * r_comp_b + + return r_s, r_b, lam + + def update(self): + """ + Update networks using Stackelberg update order. + 使用 Stackelberg 博弈顺序更新网络。 + + Order: Follower B updates first, then Leader S updates considering B's response. + 顺序：从属者 B 先更新，随后主导者 S 在考虑 B 的响应后进行更新。 + """ + if len(self.replay_buffer) < self.batch_size: + return None + + # Sample from replay buffer + # 从经验池采样 + obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, dones = \ + self.replay_buffer.sample(self.batch_size) + + # Convert to tensors + # 转换为张量 + obs_s = torch.FloatTensor(obs_s).to(self.device) + obs_b = torch.FloatTensor(obs_b).to(self.device) + act_s = torch.FloatTensor(act_s).to(self.device) + act_b = torch.FloatTensor(act_b).to(self.device) + rew_s = torch.FloatTensor(rew_s).unsqueeze(1).to(self.device) + rew_b = torch.FloatTensor(rew_b).unsqueeze(1).to(self.device) + next_obs_s = torch.FloatTensor(next_obs_s).to(self.device) + next_obs_b = torch.FloatTensor(next_obs_b).to(self.device) + dones = torch.FloatTensor(dones).unsqueeze(1).to(self.device) + + # Centralized observation and next observation + # 中心化观察与下一状态观察 + joint_obs = torch.cat([obs_s, obs_b], dim=1) + joint_next_obs = torch.cat([next_obs_s, next_obs_b], dim=1) + joint_act = torch.cat([act_s, act_b], dim=1) + + # Compute targets for critics + # 计算 Critic 的目标值 + with torch.no_grad(): + next_act_s = self.actor_s_target(next_obs_s) + next_act_b = self.actor_b_target(next_obs_b) + joint_next_act = torch.cat([next_act_s, next_act_b], dim=1) + target_q_s = rew_s + self.gamma * (1 - dones) * self.critic_s_target(joint_next_obs, joint_next_act) + target_q_b = rew_b + self.gamma * (1 - dones) * self.critic_b_target(joint_next_obs, joint_next_act) + + # --- Stackelberg: update follower B first --- + # --- Stackelberg 博弈：首先更新从属者 B --- + + # Update Critic B + # 更新 Critic B + current_q_b = self.critic_b(joint_obs, joint_act) + critic_loss_b = F.mse_loss(current_q_b, target_q_b) + self.critic_b_optimizer.zero_grad() + critic_loss_b.backward() + self.critic_b_optimizer.step() + + # Update Actor B (Follower) + # 更新 Actor B (从属者) + new_act_b = self.actor_b(obs_b) + actor_loss_b = -self.critic_b(joint_obs, torch.cat([act_s, new_act_b], dim=1)).mean() + self.actor_b_optimizer.zero_grad() + actor_loss_b.backward() + self.actor_b_optimizer.step() + + # --- Then update leader S --- + # --- 然后更新主导者 S --- + + # Re-compute follower's best response for leader's critic update + # 为主导者的 Critic 更新重新计算从属者的最佳响应 + with torch.no_grad(): + act_b_br = self.actor_b(obs_b) + joint_act_leader = torch.cat([act_s, act_b_br], dim=1) + + # Update Critic S + # 更新 Critic S + current_q_s = self.critic_s(joint_obs, joint_act_leader) + critic_loss_s = F.mse_loss(current_q_s, target_q_s) + self.critic_s_optimizer.zero_grad() + critic_loss_s.backward() + self.critic_s_optimizer.step() + + # Update Actor S (Leader) considering Follower's best response + # 考虑从属者的最佳响应，更新 Actor S (主导者) + with torch.no_grad(): + act_b_br2 = self.actor_b(obs_b) + new_act_s = self.actor_s(obs_s) + actor_loss_s = -self.critic_s(joint_obs, torch.cat([new_act_s, act_b_br2], dim=1)).mean() + self.actor_s_optimizer.zero_grad() + actor_loss_s.backward() + self.actor_s_optimizer.step() + + # Soft update target networks + # 目标网络软更新 + for target, source in [ + (self.critic_s_target, self.critic_s), + (self.critic_b_target, self.critic_b), + (self.actor_s_target, self.actor_s), + (self.actor_b_target, self.actor_b), + ]: + for tp, sp in zip(target.parameters(), source.parameters()): + tp.data.copy_(self.tau * sp.data + (1.0 - self.tau) * tp.data) + + return { + 'actor_loss_s': actor_loss_s.item(), + 'actor_loss_b': actor_loss_b.item(), + 'critic_loss_s': critic_loss_s.item(), + 'critic_loss_b': critic_loss_b.item(), + } + + def save(self, path): + """ + Save models to disk. + 将模型保存至磁盘。 + """ + os.makedirs(path, exist_ok=True) + torch.save(self.actor_s.state_dict(), os.path.join(path, "actor_s.pth")) + torch.save(self.actor_b.state_dict(), os.path.join(path, "actor_b.pth")) + torch.save(self.critic_s.state_dict(), os.path.join(path, "critic_s.pth")) + torch.save(self.critic_b.state_dict(), os.path.join(path, "critic_b.pth")) + + def load(self, path): + """ + Load models from disk. + 从磁盘加载模型。 + """ + self.actor_s.load_state_dict(torch.load(os.path.join(path, "actor_s.pth"), map_location=self.device)) + self.actor_b.load_state_dict(torch.load(os.path.join(path, "actor_b.pth"), map_location=self.device)) + self.critic_s.load_state_dict(torch.load(os.path.join(path, "critic_s.pth"), map_location=self.device)) + self.critic_b.load_state_dict(torch.load(os.path.join(path, "critic_b.pth"), map_location=self.device)) + self.actor_s_target.load_state_dict(self.actor_s.state_dict()) + self.actor_b_target.load_state_dict(self.actor_b.state_dict()) + self.critic_s_target.load_state_dict(self.critic_s.state_dict()) + self.critic_b_target.load_state_dict(self.critic_b.state_dict()) diff --git a/code/baselines/iddpg.py b/code/baselines/iddpg.py new file mode 100644 index 0000000..cce7726 --- /dev/null +++ b/code/baselines/iddpg.py @@ -0,0 +1,266 @@ +import os +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from agents.actor import Actor +from agents.replay_buffer import ReplayBuffer +from agents.noise import OUNoise + +""" +Baseline: IndependentDDPG (独立 DDPG 基线) +===================================== +Purpose (ablation): +- This baseline removes the Centralized Training Decentralized Execution (CTDE) component. +- It is used to demonstrate the necessity of joint critics that observe other agents' actions for stable training in MARL. +- 目的（消融实验）：该基线移除了中心化训练分布式执行（CTDE）组件。用于证明在多智能体强化学习中，引入能观察其他智能体动作的联合 Critic 对维持训练稳定性的必要性。 + +Difference from Co-MADDPG: +1. Critic Type: IndependentCritics are used, which only take the local observation and local action (obs_i, act_i) as input. +2. Update Order: Simultaneous independent updates for both agents. +3. 与 Co-MADDPG 的区别： + - Critic 类型：使用独立 Critic，其输入仅包含局部观察与局部动作 (obs_i, act_i)。 + - 更新顺序：两个智能体同时进行独立的更新。 + +Contribution: +- Contributes to ablation studies showing how centralized critics mitigate non-stationarity issues. +- 贡献：用于消融实验，展示中心化 Critic 如何缓解非平稳性（Non-stationarity）问题。 +""" + +class IndependentCritic(nn.Module): + """ + IndependentCritic that takes only a single agent's observation and action. + 独立 Critic，仅接收单个智能体的观察与动作。 + """ + def __init__(self, obs_dim, act_dim, hidden_sizes=[512, 512, 256]): + super().__init__() + assert len(hidden_sizes) == 3 + self.net = nn.Sequential( + nn.Linear(obs_dim + act_dim, hidden_sizes[0]), + nn.ReLU(), + nn.Linear(hidden_sizes[0], hidden_sizes[1]), + nn.ReLU(), + nn.Linear(hidden_sizes[1], hidden_sizes[2]), + nn.ReLU(), + nn.Linear(hidden_sizes[2], 1), + ) + + def forward(self, obs, act): + # Concatenate local observation and local action + # 拼接局部观察与局部动作 + x = torch.cat([obs, act], dim=1) + return self.net(x) + + +class IndependentDDPG: + """ + IndependentDDPG algorithm implementation. + 独立 DDPG 算法实现。 + """ + def __init__(self, config): + # Initialize configuration and device + # 初始化配置和设备 + self.config = config + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Hyperparameters + # 超参数 + self.gamma = config['training']['gamma'] + self.tau = config['training']['tau'] + self.batch_size = config['training']['batch_size'] + + # Dimensions + # 维度 + self.obs_dim = config['env']['num_subcarriers'] + 4 + self.act_dim = 3 + + # Hidden layer configurations + # 隐藏层配置 + hidden_a = config['network']['actor_hidden'] + hidden_c = config['network']['critic_hidden'] + + # Agent S: Local Actor and Independent Critic + # 智能体 S：局部 Actor 与独立 Critic + self.actor_s = Actor(self.obs_dim, self.act_dim, hidden_a).to(self.device) + self.actor_s_target = Actor(self.obs_dim, self.act_dim, hidden_a).to(self.device) + self.actor_s_target.load_state_dict(self.actor_s.state_dict()) + self.critic_s = IndependentCritic(self.obs_dim, self.act_dim, hidden_c).to(self.device) + self.critic_s_target = IndependentCritic(self.obs_dim, self.act_dim, hidden_c).to(self.device) + self.critic_s_target.load_state_dict(self.critic_s.state_dict()) + + # Agent B: Local Actor and Independent Critic + # 智能体 B：局部 Actor 与独立 Critic + self.actor_b = Actor(self.obs_dim, self.act_dim, hidden_a).to(self.device) + self.actor_b_target = Actor(self.obs_dim, self.act_dim, hidden_a).to(self.device) + self.actor_b_target.load_state_dict(self.actor_b.state_dict()) + self.critic_b = IndependentCritic(self.obs_dim, self.act_dim, hidden_c).to(self.device) + self.critic_b_target = IndependentCritic(self.obs_dim, self.act_dim, hidden_c).to(self.device) + self.critic_b_target.load_state_dict(self.critic_b.state_dict()) + + # Optimizers + # 优化器 + self.actor_s_optimizer = torch.optim.Adam(self.actor_s.parameters(), lr=config['training']['actor_lr']) + self.actor_b_optimizer = torch.optim.Adam(self.actor_b.parameters(), lr=config['training']['actor_lr']) + self.critic_s_optimizer = torch.optim.Adam(self.critic_s.parameters(), lr=config['training']['critic_lr']) + self.critic_b_optimizer = torch.optim.Adam(self.critic_b.parameters(), lr=config['training']['critic_lr']) + + # Shared replay buffer + # 共享重放池 + self.replay_buffer = ReplayBuffer(config['training']['buffer_capacity']) + + # Noise for exploration + # 探索噪声 + self.noise_s = OUNoise(self.act_dim, theta=config['training']['ou_theta'], + sigma_init=config['training']['ou_sigma_init'], + sigma_min=config['training']['ou_sigma_min']) + self.noise_b = OUNoise(self.act_dim, theta=config['training']['ou_theta'], + sigma_init=config['training']['ou_sigma_init'], + sigma_min=config['training']['ou_sigma_min']) + + def select_action(self, obs_s, obs_b, explore=True): + """ + Select actions for both agents. + 为两个智能体选择动作。 + """ + self.actor_s.eval() + self.actor_b.eval() + with torch.no_grad(): + obs_s_t = torch.FloatTensor(obs_s).unsqueeze(0).to(self.device) + obs_b_t = torch.FloatTensor(obs_b).unsqueeze(0).to(self.device) + act_s = self.actor_s(obs_s_t).cpu().numpy()[0] + act_b = self.actor_b(obs_b_t).cpu().numpy()[0] + self.actor_s.train() + self.actor_b.train() + + if explore: + # Apply OU noise + # 应用 OU 噪声 + act_s = np.clip(act_s + self.noise_s.sample(), 0.0, 1.0) + act_b = np.clip(act_b + self.noise_b.sample(), 0.0, 1.0) + else: + act_s = np.clip(act_s, 0.0, 1.0) + act_b = np.clip(act_b, 0.0, 1.0) + + return act_s, act_b + + def compute_rewards(self, qoe_s, qoe_b, qoe_sys): + """ + Compute rewards based on independent competitive behavior (λ=0). + 基于独立的竞争行为计算奖励 (λ=0)。 + + Formula: r_i = comp_self * qoe_i + comp_sys * qoe_sys + 公式说明：独立模式下默认为纯竞争，每个智能体仅优化自身效用及系统整体惩罚。 + """ + lam = 0.0 + r_s = self.config['reward']['comp_self'] * qoe_s + self.config['reward']['comp_sys'] * qoe_sys + r_b = self.config['reward']['comp_self'] * qoe_b + self.config['reward']['comp_sys'] * qoe_sys + return r_s, r_b, lam + + def update(self): + """ + Update each agent independently and simultaneously. + 独立且同步地更新每个智能体。 + """ + if len(self.replay_buffer) < self.batch_size: + return None + + # Sample batch + # 采样批量数据 + obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, dones = \ + self.replay_buffer.sample(self.batch_size) + + # To tensors + # 转换为张量 + obs_s = torch.FloatTensor(obs_s).to(self.device) + obs_b = torch.FloatTensor(obs_b).to(self.device) + act_s = torch.FloatTensor(act_s).to(self.device) + act_b = torch.FloatTensor(act_b).to(self.device) + rew_s = torch.FloatTensor(rew_s).unsqueeze(1).to(self.device) + rew_b = torch.FloatTensor(rew_b).unsqueeze(1).to(self.device) + next_obs_s = torch.FloatTensor(next_obs_s).to(self.device) + next_obs_b = torch.FloatTensor(next_obs_b).to(self.device) + dones = torch.FloatTensor(dones).unsqueeze(1).to(self.device) + + # --- Update Agent S (independent) --- + # --- 独立更新智能体 S --- + with torch.no_grad(): + # Critic target only uses local next observation and action + # Critic 目标仅使用局部下一状态观察与动作 + next_act_s = self.actor_s_target(next_obs_s) + target_q_s = rew_s + self.gamma * (1 - dones) * self.critic_s_target(next_obs_s, next_act_s) + + current_q_s = self.critic_s(obs_s, act_s) + critic_loss_s = F.mse_loss(current_q_s, target_q_s) + self.critic_s_optimizer.zero_grad() + critic_loss_s.backward() + self.critic_s_optimizer.step() + + new_act_s = self.actor_s(obs_s) + actor_loss_s = -self.critic_s(obs_s, new_act_s).mean() + self.actor_s_optimizer.zero_grad() + actor_loss_s.backward() + self.actor_s_optimizer.step() + + # --- Update Agent B (independent) --- + # --- 独立更新智能体 B --- + with torch.no_grad(): + # Critic target only uses local next observation and action + # Critic 目标仅使用局部下一状态观察与动作 + next_act_b = self.actor_b_target(next_obs_b) + target_q_b = rew_b + self.gamma * (1 - dones) * self.critic_b_target(next_obs_b, next_act_b) + + current_q_b = self.critic_b(obs_b, act_b) + critic_loss_b = F.mse_loss(current_q_b, target_q_b) + self.critic_b_optimizer.zero_grad() + critic_loss_b.backward() + self.critic_b_optimizer.step() + + new_act_b = self.actor_b(obs_b) + actor_loss_b = -self.critic_b(obs_b, new_act_b).mean() + self.actor_b_optimizer.zero_grad() + actor_loss_b.backward() + self.actor_b_optimizer.step() + + # Soft update targets for both agents + # 软更新两个智能体的目标网络 + for target, source in [ + (self.critic_s_target, self.critic_s), + (self.critic_b_target, self.critic_b), + (self.actor_s_target, self.actor_s), + (self.actor_b_target, self.actor_b), + ]: + for tp, sp in zip(target.parameters(), source.parameters()): + tp.data.copy_(self.tau * sp.data + (1.0 - self.tau) * tp.data) + + return { + 'actor_loss_s': actor_loss_s.item(), + 'actor_loss_b': actor_loss_b.item(), + 'critic_loss_s': critic_loss_s.item(), + 'critic_loss_b': critic_loss_b.item(), + } + + def save(self, path): + """ + Save models. + 保存模型。 + """ + os.makedirs(path, exist_ok=True) + torch.save(self.actor_s.state_dict(), os.path.join(path, "actor_s.pth")) + torch.save(self.actor_b.state_dict(), os.path.join(path, "actor_b.pth")) + torch.save(self.critic_s.state_dict(), os.path.join(path, "critic_s.pth")) + torch.save(self.critic_b.state_dict(), os.path.join(path, "critic_b.pth")) + + def load(self, path): + """ + Load models. + 加载模型。 + """ + self.actor_s.load_state_dict(torch.load(os.path.join(path, "actor_s.pth"), map_location=self.device)) + self.actor_b.load_state_dict(torch.load(os.path.join(path, "actor_b.pth"), map_location=self.device)) + self.critic_s.load_state_dict(torch.load(os.path.join(path, "critic_s.pth"), map_location=self.device)) + self.critic_b.load_state_dict(torch.load(os.path.join(path, "critic_b.pth"), map_location=self.device)) + self.actor_s_target.load_state_dict(self.actor_s.state_dict()) + self.actor_b_target.load_state_dict(self.actor_b.state_dict()) + self.critic_s_target.load_state_dict(self.critic_s.state_dict()) + self.critic_b_target.load_state_dict(self.critic_b.state_dict()) diff --git a/code/baselines/pure_comp.py b/code/baselines/pure_comp.py new file mode 100644 index 0000000..73318ac --- /dev/null +++ b/code/baselines/pure_comp.py @@ -0,0 +1,245 @@ +import os +import torch +import torch.nn.functional as F +import numpy as np +from agents.actor import Actor +from agents.critic import Critic +from agents.replay_buffer import ReplayBuffer +from agents.noise import OUNoise + +""" +Baseline: PureCompetitive (纯竞争基线) +===================================== +Purpose (ablation): +- This baseline removes the cooperative component from the MADDPG framework. +- It serves as an ablation study to demonstrate that pure competition (λ=0) leads to resource wastage and suboptimal system-wide utility. +- 目的（消融实验）：该基线移除了 MADDPG 框架中的协作成分。作为消融实验，用于证明纯竞争模式（λ=0）会导致资源浪费和系统级效用降低。 + +Difference from Co-MADDPG: +1. Lambda (λ): Fixed at 0.0 (pure competition), whereas Co-MADDPG uses dynamic λ. +2. Update Order: Uses simultaneous updates for both actors, whereas Co-MADDPG uses Stackelberg update order. +3. 与 Co-MADDPG 的区别： + - Lambda (λ): 固定为 0.0（纯竞争），而 Co-MADDPG 使用动态 λ。 + - 更新顺序：两个参与者同时更新（Simultaneous Update），而 Co-MADDPG 使用 Stackelberg 博弈更新顺序。 + +Contribution: +- Contributes to comparison figures showing the "Price of Anarchy" in resource allocation. +- 贡献：用于对比图表，展示资源分配中的“无政府代价”。 +""" + +class PureCompetitive: + """ + PureCompetitive algorithm implementation. + 纯竞争算法实现。 + """ + def __init__(self, config): + # Initialize configuration and device + # 初始化配置和设备 + self.config = config + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Hyperparameters: Gamma (discount), Tau (soft update), Batch Size + # 超参数：折扣因子、软更新系数、批量大小 + self.gamma = config['training']['gamma'] + self.tau = config['training']['tau'] + self.batch_size = config['training']['batch_size'] + + # Dimensions: State (subcarriers + 4), Action (3) + # 维度：状态（子载波 + 4）、动作（3） + self.obs_dim = config['env']['num_subcarriers'] + 4 + self.act_dim = 3 + + # Agents: Semantic (s) and Traditional (b) actors and target networks + # 智能体：语义 (s) 与 传统 (b) 参与者的 Actor 及其目标网络 + self.actor_s = Actor(self.obs_dim, self.act_dim, config['network']['actor_hidden']).to(self.device) + self.actor_b = Actor(self.obs_dim, self.act_dim, config['network']['actor_hidden']).to(self.device) + self.actor_s_target = Actor(self.obs_dim, self.act_dim, config['network']['actor_hidden']).to(self.device) + self.actor_b_target = Actor(self.obs_dim, self.act_dim, config['network']['actor_hidden']).to(self.device) + self.actor_s_target.load_state_dict(self.actor_s.state_dict()) + self.actor_b_target.load_state_dict(self.actor_b.state_dict()) + + # Joint Critics: Uses Centralized Training (obs_dim*2, act_dim*2) + # 联合 Critic：使用中心化训练（输入为两体观察与动作的并集） + self.critic_s = Critic(self.obs_dim*2, self.act_dim*2, config['network']['critic_hidden']).to(self.device) + self.critic_b = Critic(self.obs_dim*2, self.act_dim*2, config['network']['critic_hidden']).to(self.device) + self.critic_s_target = Critic(self.obs_dim*2, self.act_dim*2, config['network']['critic_hidden']).to(self.device) + self.critic_b_target = Critic(self.obs_dim*2, self.act_dim*2, config['network']['critic_hidden']).to(self.device) + self.critic_s_target.load_state_dict(self.critic_s.state_dict()) + self.critic_b_target.load_state_dict(self.critic_b.state_dict()) + + # Optimizers for all networks + # 所有网络的优化器 + self.actor_s_optimizer = torch.optim.Adam(self.actor_s.parameters(), lr=config['training']['actor_lr']) + self.actor_b_optimizer = torch.optim.Adam(self.actor_b.parameters(), lr=config['training']['actor_lr']) + self.critic_s_optimizer = torch.optim.Adam(self.critic_s.parameters(), lr=config['training']['critic_lr']) + self.critic_b_optimizer = torch.optim.Adam(self.critic_b.parameters(), lr=config['training']['critic_lr']) + + # Experience Replay and Noise for exploration + # 经验重放池与用于探索的噪声 + self.replay_buffer = ReplayBuffer(config['training']['buffer_capacity']) + self.noise_s = OUNoise(self.act_dim, theta=config['training']['ou_theta'], sigma_init=config['training']['ou_sigma_init'], sigma_min=config['training']['ou_sigma_min']) + self.noise_b = OUNoise(self.act_dim, theta=config['training']['ou_theta'], sigma_init=config['training']['ou_sigma_init'], sigma_min=config['training']['ou_sigma_min']) + + def select_action(self, obs_s, obs_b, explore=True): + """ + Select actions for both agents given observations. + 根据观察结果为两个智能体选择动作。 + """ + obs_s = torch.FloatTensor(obs_s).unsqueeze(0).to(self.device) + obs_b = torch.FloatTensor(obs_b).unsqueeze(0).to(self.device) + + self.actor_s.eval() + self.actor_b.eval() + with torch.no_grad(): + # Forward pass through actors + # Actor 前向传播 + act_s = self.actor_s(obs_s).cpu().numpy()[0] + act_b = self.actor_b(obs_b).cpu().numpy()[0] + self.actor_s.train() + self.actor_b.train() + + if explore: + # Apply OU noise for exploration + # 应用 OU 噪声进行探索 + act_s = np.clip(act_s + self.noise_s.sample(), 0.0, 1.0) + act_b = np.clip(act_b + self.noise_b.sample(), 0.0, 1.0) + + return act_s, act_b + + def compute_rewards(self, qoe_s, qoe_b, qoe_sys): + """ + Compute rewards based on pure competition (λ=0). + 基于纯竞争计算奖励 (λ=0)。 + + Formula: r_i = comp_self * qoe_i + comp_sys * qoe_sys + 公式说明：由于 λ=0，奖励完全由竞争项组成，仅考虑自身 QoE 以及系统总 QoE 的惩罚项。 + """ + lam = 0.0 + r_s = self.config['reward']['comp_self'] * qoe_s + self.config['reward']['comp_sys'] * qoe_sys + r_b = self.config['reward']['comp_self'] * qoe_b + self.config['reward']['comp_sys'] * qoe_sys + return r_s, r_b, lam + + def update(self): + """ + Update the networks using sampled experiences. + 使用采样的经验更新网络。 + + Update order: Simultaneous updates (both actors update based on current policy of the other). + 更新顺序：同时更新（两个 Actor 基于对方当前的策略进行更新）。 + """ + if len(self.replay_buffer) < self.batch_size: + return None + + # Sample batch from replay buffer + # 从重放池采样批量数据 + obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, dones = self.replay_buffer.sample(self.batch_size) + + # Convert to tensors + # 转换为张量 + obs_s = torch.FloatTensor(obs_s).to(self.device) + obs_b = torch.FloatTensor(obs_b).to(self.device) + act_s = torch.FloatTensor(act_s).to(self.device) + act_b = torch.FloatTensor(act_b).to(self.device) + rew_s = torch.FloatTensor(rew_s).unsqueeze(1).to(self.device) + rew_b = torch.FloatTensor(rew_b).unsqueeze(1).to(self.device) + next_obs_s = torch.FloatTensor(next_obs_s).to(self.device) + next_obs_b = torch.FloatTensor(next_obs_b).to(self.device) + dones = torch.FloatTensor(dones).unsqueeze(1).to(self.device) + + # Centralized observations and actions + # 中心化观察与动作 + joint_obs = torch.cat([obs_s, obs_b], dim=1) + joint_next_obs = torch.cat([next_obs_s, next_obs_b], dim=1) + joint_act = torch.cat([act_s, act_b], dim=1) + + # 1. Critics Update (1. Critic 更新) + with torch.no_grad(): + # Get target actions for next state + # 获取下一状态的目标动作 + next_act_s = self.actor_s_target(next_obs_s) + next_act_b = self.actor_b_target(next_obs_b) + joint_next_act = torch.cat([next_act_s, next_act_b], dim=1) + + # Compute target Q values + # 计算目标 Q 值 + target_q_s = rew_s + self.gamma * (1 - dones) * self.critic_s_target(joint_next_obs, joint_next_act) + target_q_b = rew_b + self.gamma * (1 - dones) * self.critic_b_target(joint_next_obs, joint_next_act) + + # Compute current Q values and MSE loss + # 计算当前 Q 值与均方误差损失 + current_q_s = self.critic_s(joint_obs, joint_act) + current_q_b = self.critic_b(joint_obs, joint_act) + + critic_loss_s = F.mse_loss(current_q_s, target_q_s) + critic_loss_b = F.mse_loss(current_q_b, target_q_b) + + # Backpropagation for critics + # Critic 的反向传播 + self.critic_s_optimizer.zero_grad() + critic_loss_s.backward() + self.critic_s_optimizer.step() + + self.critic_b_optimizer.zero_grad() + critic_loss_b.backward() + self.critic_b_optimizer.step() + + # 2. Actors Update (Simultaneous) (2. Actor 更新 - 同时进行) + new_act_s = self.actor_s(obs_s) + new_act_b = self.actor_b(obs_b) + + # Calculate policy loss using joint critic + # 使用联合 Critic 计算策略损失 + actor_loss_s = -self.critic_s(joint_obs, torch.cat([new_act_s, act_b], dim=1)).mean() + actor_loss_b = -self.critic_b(joint_obs, torch.cat([act_s, new_act_b], dim=1)).mean() + + # Backpropagation for actors + # Actor 的反向传播 + self.actor_s_optimizer.zero_grad() + actor_loss_s.backward() + self.actor_s_optimizer.step() + + self.actor_b_optimizer.zero_grad() + actor_loss_b.backward() + self.actor_b_optimizer.step() + + # 3. Soft Target Networks Update (3. 目标网络软更新) + for target_param, param in zip(self.critic_s_target.parameters(), self.critic_s.parameters()): + target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data) + for target_param, param in zip(self.critic_b_target.parameters(), self.critic_b.parameters()): + target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data) + for target_param, param in zip(self.actor_s_target.parameters(), self.actor_s.parameters()): + target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data) + for target_param, param in zip(self.actor_b_target.parameters(), self.actor_b.parameters()): + target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data) + + return { + 'actor_loss_s': actor_loss_s.item(), + 'actor_loss_b': actor_loss_b.item(), + 'critic_loss_s': critic_loss_s.item(), + 'critic_loss_b': critic_loss_b.item() + } + + def save(self, path): + """ + Save models to disk. + 将模型保存至磁盘。 + """ + os.makedirs(path, exist_ok=True) + torch.save(self.actor_s.state_dict(), os.path.join(path, "actor_s.pth")) + torch.save(self.actor_b.state_dict(), os.path.join(path, "actor_b.pth")) + torch.save(self.critic_s.state_dict(), os.path.join(path, "critic_s.pth")) + torch.save(self.critic_b.state_dict(), os.path.join(path, "critic_b.pth")) + + def load(self, path): + """ + Load models from disk. + 从磁盘加载模型。 + """ + self.actor_s.load_state_dict(torch.load(os.path.join(path, "actor_s.pth"), map_location=self.device)) + self.actor_b.load_state_dict(torch.load(os.path.join(path, "actor_b.pth"), map_location=self.device)) + self.critic_s.load_state_dict(torch.load(os.path.join(path, "critic_s.pth"), map_location=self.device)) + self.critic_b.load_state_dict(torch.load(os.path.join(path, "critic_b.pth"), map_location=self.device)) + self.actor_s_target.load_state_dict(self.actor_s.state_dict()) + self.actor_b_target.load_state_dict(self.actor_b.state_dict()) + self.critic_s_target.load_state_dict(self.critic_s.state_dict()) + self.critic_b_target.load_state_dict(self.critic_b.state_dict()) diff --git a/code/baselines/pure_coop.py b/code/baselines/pure_coop.py new file mode 100644 index 0000000..6eed1ae --- /dev/null +++ b/code/baselines/pure_coop.py @@ -0,0 +1,245 @@ +import os +import torch +import torch.nn.functional as F +import numpy as np +from agents.actor import Actor +from agents.critic import Critic +from agents.replay_buffer import ReplayBuffer +from agents.noise import OUNoise + +""" +Baseline: PureCooperative (纯协作基线) +===================================== +Purpose (ablation): +- This baseline removes the competitive component from the MADDPG framework. +- It serves as an ablation study to demonstrate the necessity of competitive modeling (λ < 1) for system performance. +- 目的（消融实验）：该基线移除了 MADDPG 框架中的竞争成分。作为消融实验，用于证明在系统中引入竞争建模（λ < 1）对性能提升的必要性。 + +Difference from Co-MADDPG: +1. Lambda (λ): Fixed at 1.0 (pure cooperation), whereas Co-MADDPG uses dynamic λ. +2. Update Order: Uses simultaneous updates for both actors, whereas Co-MADDPG uses Stackelberg update order. +3. 与 Co-MADDPG 的区别： + - Lambda (λ): 固定为 1.0（纯协作），而 Co-MADDPG 使用动态 λ。 + - 更新顺序：两个参与者同时更新（Simultaneous Update），而 Co-MADDPG 使用 Stackelberg 博弈更新顺序。 + +Contribution: +- Contributes to performance comparison figures and tables (e.g., convergence speed and final QoE) to show how pure cooperation handles resource conflicts. +- 贡献：用于性能对比图表（如收敛速度和最终 QoE），展示纯协作模式在处理资源冲突时的表现。 +""" + +class PureCooperative: + """ + PureCooperative algorithm implementation. + 纯协作算法实现。 + """ + def __init__(self, config): + # Initialize configuration and device + # 初始化配置和设备 + self.config = config + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Hyperparameters: Gamma (discount), Tau (soft update), Batch Size + # 超参数：折扣因子、软更新系数、批量大小 + self.gamma = config['training']['gamma'] + self.tau = config['training']['tau'] + self.batch_size = config['training']['batch_size'] + + # Dimensions: State (subcarriers + 4), Action (3) + # 维度：状态（子载波 + 4）、动作（3） + self.obs_dim = config['env']['num_subcarriers'] + 4 + self.act_dim = 3 + + # Agents: Semantic (s) and Traditional (b) actors and target networks + # 智能体：语义 (s) 与 传统 (b) 参与者的 Actor 及其目标网络 + self.actor_s = Actor(self.obs_dim, self.act_dim, config['network']['actor_hidden']).to(self.device) + self.actor_b = Actor(self.obs_dim, self.act_dim, config['network']['actor_hidden']).to(self.device) + self.actor_s_target = Actor(self.obs_dim, self.act_dim, config['network']['actor_hidden']).to(self.device) + self.actor_b_target = Actor(self.obs_dim, self.act_dim, config['network']['actor_hidden']).to(self.device) + self.actor_s_target.load_state_dict(self.actor_s.state_dict()) + self.actor_b_target.load_state_dict(self.actor_b.state_dict()) + + # Joint Critics: Uses Centralized Training (obs_dim*2, act_dim*2) + # 联合 Critic：使用中心化训练（输入为两体观察与动作的并集） + self.critic_s = Critic(self.obs_dim*2, self.act_dim*2, config['network']['critic_hidden']).to(self.device) + self.critic_b = Critic(self.obs_dim*2, self.act_dim*2, config['network']['critic_hidden']).to(self.device) + self.critic_s_target = Critic(self.obs_dim*2, self.act_dim*2, config['network']['critic_hidden']).to(self.device) + self.critic_b_target = Critic(self.obs_dim*2, self.act_dim*2, config['network']['critic_hidden']).to(self.device) + self.critic_s_target.load_state_dict(self.critic_s.state_dict()) + self.critic_b_target.load_state_dict(self.critic_b.state_dict()) + + # Optimizers for all networks + # 所有网络的优化器 + self.actor_s_optimizer = torch.optim.Adam(self.actor_s.parameters(), lr=config['training']['actor_lr']) + self.actor_b_optimizer = torch.optim.Adam(self.actor_b.parameters(), lr=config['training']['actor_lr']) + self.critic_s_optimizer = torch.optim.Adam(self.critic_s.parameters(), lr=config['training']['critic_lr']) + self.critic_b_optimizer = torch.optim.Adam(self.critic_b.parameters(), lr=config['training']['critic_lr']) + + # Experience Replay and Noise for exploration + # 经验重放池与用于探索的噪声 + self.replay_buffer = ReplayBuffer(config['training']['buffer_capacity']) + self.noise_s = OUNoise(self.act_dim, theta=config['training']['ou_theta'], sigma_init=config['training']['ou_sigma_init'], sigma_min=config['training']['ou_sigma_min']) + self.noise_b = OUNoise(self.act_dim, theta=config['training']['ou_theta'], sigma_init=config['training']['ou_sigma_init'], sigma_min=config['training']['ou_sigma_min']) + + def select_action(self, obs_s, obs_b, explore=True): + """ + Select actions for both agents given observations. + 根据观察结果为两个智能体选择动作。 + """ + obs_s = torch.FloatTensor(obs_s).unsqueeze(0).to(self.device) + obs_b = torch.FloatTensor(obs_b).unsqueeze(0).to(self.device) + + self.actor_s.eval() + self.actor_b.eval() + with torch.no_grad(): + # Forward pass through actors + # Actor 前向传播 + act_s = self.actor_s(obs_s).cpu().numpy()[0] + act_b = self.actor_b(obs_b).cpu().numpy()[0] + self.actor_s.train() + self.actor_b.train() + + if explore: + # Apply OU noise for exploration + # 应用 OU 噪声进行探索 + act_s = np.clip(act_s + self.noise_s.sample(), 0.0, 1.0) + act_b = np.clip(act_b + self.noise_b.sample(), 0.0, 1.0) + + return act_s, act_b + + def compute_rewards(self, qoe_s, qoe_b, qoe_sys): + """ + Compute rewards based on pure cooperation (λ=1). + 基于纯协作计算奖励 (λ=1)。 + + Formula: r_i = coop_self * qoe_i + coop_other * qoe_j + coop_sys * qoe_sys + 公式说明：由于 λ=1，奖励完全由协作项组成，考虑自身 QoE、对方 QoE 以及系统总 QoE。 + """ + lam = 1.0 + r_s = self.config['reward']['coop_self'] * qoe_s + self.config['reward']['coop_other'] * qoe_b + self.config['reward']['coop_sys'] * qoe_sys + r_b = self.config['reward']['coop_self'] * qoe_b + self.config['reward']['coop_other'] * qoe_s + self.config['reward']['coop_sys'] * qoe_sys + return r_s, r_b, lam + + def update(self): + """ + Update the networks using sampled experiences. + 使用采样的经验更新网络。 + + Update order: Simultaneous updates (both actors update based on current policy of the other). + 更新顺序：同时更新（两个 Actor 基于对方当前的策略进行更新）。 + """ + if len(self.replay_buffer) < self.batch_size: + return None + + # Sample batch from replay buffer + # 从重放池采样批量数据 + obs_s, obs_b, act_s, act_b, rew_s, rew_b, next_obs_s, next_obs_b, dones = self.replay_buffer.sample(self.batch_size) + + # Convert to tensors + # 转换为张量 + obs_s = torch.FloatTensor(obs_s).to(self.device) + obs_b = torch.FloatTensor(obs_b).to(self.device) + act_s = torch.FloatTensor(act_s).to(self.device) + act_b = torch.FloatTensor(act_b).to(self.device) + rew_s = torch.FloatTensor(rew_s).unsqueeze(1).to(self.device) + rew_b = torch.FloatTensor(rew_b).unsqueeze(1).to(self.device) + next_obs_s = torch.FloatTensor(next_obs_s).to(self.device) + next_obs_b = torch.FloatTensor(next_obs_b).to(self.device) + dones = torch.FloatTensor(dones).unsqueeze(1).to(self.device) + + # Centralized observations and actions + # 中心化观察与动作 + joint_obs = torch.cat([obs_s, obs_b], dim=1) + joint_next_obs = torch.cat([next_obs_s, next_obs_b], dim=1) + joint_act = torch.cat([act_s, act_b], dim=1) + + # 1. Critics Update (1. Critic 更新) + with torch.no_grad(): + # Get target actions for next state + # 获取下一状态的目标动作 + next_act_s = self.actor_s_target(next_obs_s) + next_act_b = self.actor_b_target(next_obs_b) + joint_next_act = torch.cat([next_act_s, next_act_b], dim=1) + + # Compute target Q values + # 计算目标 Q 值 + target_q_s = rew_s + self.gamma * (1 - dones) * self.critic_s_target(joint_next_obs, joint_next_act) + target_q_b = rew_b + self.gamma * (1 - dones) * self.critic_b_target(joint_next_obs, joint_next_act) + + # Compute current Q values and MSE loss + # 计算当前 Q 值与均方误差损失 + current_q_s = self.critic_s(joint_obs, joint_act) + current_q_b = self.critic_b(joint_obs, joint_act) + + critic_loss_s = F.mse_loss(current_q_s, target_q_s) + critic_loss_b = F.mse_loss(current_q_b, target_q_b) + + # Backpropagation for critics + # Critic 的反向传播 + self.critic_s_optimizer.zero_grad() + critic_loss_s.backward() + self.critic_s_optimizer.step() + + self.critic_b_optimizer.zero_grad() + critic_loss_b.backward() + self.critic_b_optimizer.step() + + # 2. Actors Update (Simultaneous) (2. Actor 更新 - 同时进行) + new_act_s = self.actor_s(obs_s) + new_act_b = self.actor_b(obs_b) + + # Calculate policy loss using joint critic + # 使用联合 Critic 计算策略损失 + actor_loss_s = -self.critic_s(joint_obs, torch.cat([new_act_s, act_b], dim=1)).mean() + actor_loss_b = -self.critic_b(joint_obs, torch.cat([act_s, new_act_b], dim=1)).mean() + + # Backpropagation for actors + # Actor 的反向传播 + self.actor_s_optimizer.zero_grad() + actor_loss_s.backward() + self.actor_s_optimizer.step() + + self.actor_b_optimizer.zero_grad() + actor_loss_b.backward() + self.actor_b_optimizer.step() + + # 3. Soft Target Networks Update (3. 目标网络软更新) + for target_param, param in zip(self.critic_s_target.parameters(), self.critic_s.parameters()): + target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data) + for target_param, param in zip(self.critic_b_target.parameters(), self.critic_b.parameters()): + target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data) + for target_param, param in zip(self.actor_s_target.parameters(), self.actor_s.parameters()): + target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data) + for target_param, param in zip(self.actor_b_target.parameters(), self.actor_b.parameters()): + target_param.data.copy_(self.tau * param.data + (1.0 - self.tau) * target_param.data) + + return { + 'actor_loss_s': actor_loss_s.item(), + 'actor_loss_b': actor_loss_b.item(), + 'critic_loss_s': critic_loss_s.item(), + 'critic_loss_b': critic_loss_b.item() + } + + def save(self, path): + """ + Save models to disk. + 将模型保存至磁盘。 + """ + os.makedirs(path, exist_ok=True) + torch.save(self.actor_s.state_dict(), os.path.join(path, "actor_s.pth")) + torch.save(self.actor_b.state_dict(), os.path.join(path, "actor_b.pth")) + torch.save(self.critic_s.state_dict(), os.path.join(path, "critic_s.pth")) + torch.save(self.critic_b.state_dict(), os.path.join(path, "critic_b.pth")) + + def load(self, path): + """ + Load models from disk. + 从磁盘加载模型。 + """ + self.actor_s.load_state_dict(torch.load(os.path.join(path, "actor_s.pth"), map_location=self.device)) + self.actor_b.load_state_dict(torch.load(os.path.join(path, "actor_b.pth"), map_location=self.device)) + self.critic_s.load_state_dict(torch.load(os.path.join(path, "critic_s.pth"), map_location=self.device)) + self.critic_b.load_state_dict(torch.load(os.path.join(path, "critic_b.pth"), map_location=self.device)) + self.actor_s_target.load_state_dict(self.actor_s.state_dict()) + self.actor_b_target.load_state_dict(self.actor_b.state_dict()) + self.critic_s_target.load_state_dict(self.critic_s.state_dict()) + self.critic_b_target.load_state_dict(self.critic_b.state_dict()) diff --git a/code/baselines/semantic_only.py b/code/baselines/semantic_only.py new file mode 100644 index 0000000..f550db7 --- /dev/null +++ b/code/baselines/semantic_only.py @@ -0,0 +1,238 @@ +import os +import random +from collections import deque +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from agents.actor import Actor +from agents.noise import OUNoise + +""" +Baseline: SemanticOnly (仅语义基线) +===================================== +Purpose (ablation): +- This baseline removes the heterogeneous treatment of different user groups. +- It treats all users as semantic users and uses a single DDPG policy to control both groups. +- It serves as an ablation study to demonstrate the benefit of having heterogeneous, specialized policies for semantic vs. traditional users. +- 目的（消融实验）：该基线移除了对不同用户组的异构处理。它将所有用户视为语义用户，并使用单一的 DDPG 策略同时控制两个用户组。作为消融实验，用于证明为语义用户和传统用户分别设计专门的异构策略的收益。 + +Difference from Co-MADDPG: +1. Heterogeneity: Homogeneous policy (all semantic) vs Heterogeneous policies. +2. Architecture: Single DDPG agent for both groups vs Multi-agent (Co-MADDPG). +3. 与 Co-MADDPG 的区别： + - 异构性：同构策略（全部视为语义用户） vs 异构策略。 + - 架构：单 DDPG 智能体控制两组 vs 多智能体 (Co-MADDPG)。 + +Contribution: +- Contributes to performance analysis regarding user heterogeneity and specialized resource allocation. +- 贡献：用于关于用户异构性和专门化资源分配的性能分析。 +""" + +class SemanticCritic(nn.Module): + """ + Single-agent critic: observation + action → Q-value. + 单智能体 Critic：观察 + 动作 → Q 值。 + """ + def __init__(self, obs_dim, act_dim, hidden_sizes=[256, 256, 128]): + super().__init__() + assert len(hidden_sizes) == 3 + self.net = nn.Sequential( + nn.Linear(obs_dim + act_dim, hidden_sizes[0]), + nn.ReLU(), + nn.Linear(hidden_sizes[0], hidden_sizes[1]), + nn.ReLU(), + nn.Linear(hidden_sizes[1], hidden_sizes[2]), + nn.ReLU(), + nn.Linear(hidden_sizes[2], 1), + ) + + def forward(self, obs, act): + # Forward pass for single agent + # 单智能体前向传播 + return self.net(torch.cat([obs, act], dim=1)) + + +class SemanticBuffer: + """ + Replay buffer for SemanticOnly baseline. + 仅语义基线的重放池。 + + Wrapper that accepts the 9-arg multi-agent push but stores single-agent transitions. + 接收多智能体 9 参数 push 请求，但内部存储单智能体转换数据。 + """ + def __init__(self, capacity): + self.buffer = deque(maxlen=capacity) + + def push(self, obs_s, obs_b, act_s, act_b, rew_s, rew_b, + next_obs_s, next_obs_b, done=False): + """ + Store only semantic agent's observation/action and average reward. + 仅存储语义智能体的观察/动作以及平均奖励。 + """ + self.buffer.append(( + np.asarray(obs_s, dtype=np.float32), + np.asarray(act_s, dtype=np.float32), + float(0.5 * (rew_s + rew_b)), + np.asarray(next_obs_s, dtype=np.float32), + float(done), + )) + + def sample(self, batch_size): + """Sample batch.""" + batch = random.sample(self.buffer, batch_size) + obs, act, rew, next_obs, dones = zip(*batch) + return (np.array(obs), np.array(act), np.array(rew, dtype=np.float32), + np.array(next_obs), np.array(dones, dtype=np.float32)) + + def __len__(self): + return len(self.buffer) + + +class SemanticOnly: + """ + SemanticOnly algorithm implementation. + 仅语义算法实现。 + """ + def __init__(self, config): + # Initialize configuration and device + # 初始化配置和设备 + self.config = config + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Hyperparameters + # 超参数 + self.gamma = config['training']['gamma'] + self.tau = config['training']['tau'] + self.batch_size = config['training']['batch_size'] + + # Dimensions + # 维度 + self.obs_dim = config['env']['num_subcarriers'] + 4 + self.act_dim = 3 + + # Network configurations + # 网络配置 + hidden_a = config['network']['actor_hidden'] + critic_hidden = [256, 256, 128] + + # Single Actor and Critic policy + # 单一 Actor 与 Critic 策略 + self.actor = Actor(self.obs_dim, self.act_dim, hidden_a).to(self.device) + self.actor_target = Actor(self.obs_dim, self.act_dim, hidden_a).to(self.device) + self.actor_target.load_state_dict(self.actor.state_dict()) + + self.critic = SemanticCritic(self.obs_dim, self.act_dim, critic_hidden).to(self.device) + self.critic_target = SemanticCritic(self.obs_dim, self.act_dim, critic_hidden).to(self.device) + self.critic_target.load_state_dict(self.critic.state_dict()) + + # Optimizers + # 优化器 + self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=config['training']['actor_lr']) + self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=config['training']['critic_lr']) + + # Buffer and Noise + # 重放池与噪声 + self.replay_buffer = SemanticBuffer(config['training']['buffer_capacity']) + self.noise_s = OUNoise(self.act_dim, theta=config['training']['ou_theta'], + sigma_init=config['training']['ou_sigma_init'], + sigma_min=config['training']['ou_sigma_min']) + # Alias for compatibility with training loop + # 与训练循环兼容的别名 + self.noise_b = self.noise_s + + def select_action(self, obs_s, obs_b, explore=True): + """ + Select actions for both groups using the same policy. + 使用相同策略为两组用户选择动作。 + """ + self.actor.eval() + with torch.no_grad(): + obs_t = torch.FloatTensor(obs_s).unsqueeze(0).to(self.device) + act = self.actor(obs_t).cpu().numpy()[0] + self.actor.train() + + if explore: + # Apply OU noise + # 应用 OU 噪声 + act = np.clip(act + self.noise_s.sample(), 0.0, 1.0) + else: + act = np.clip(act, 0.0, 1.0) + + # Return the same action for both groups + # 为两组用户返回相同的动作 + return act.copy(), act.copy() + + def compute_rewards(self, qoe_s, qoe_b, qoe_sys): + """ + Compute rewards assuming full cooperation (λ=1). + 假设完全协作 (λ=1) 计算奖励。 + + Formula: r = 0.5 * (qoe_s + qoe_b) + 公式说明：由于全部视为语义用户，目标是最大化整体 QoE。 + """ + lam = 1.0 + r = 0.5 * (qoe_s + qoe_b) + return r, r, lam + + def update(self): + """ + Update the single DDPG agent. + 更新单个 DDPG 智能体。 + """ + if len(self.replay_buffer) < self.batch_size: + return None + + # Sample from buffer + # 从重放池采样 + obs, act, rew, next_obs, dones = self.replay_buffer.sample(self.batch_size) + + # To tensors + # 转换为张量 + obs_t = torch.FloatTensor(obs).to(self.device) + act_t = torch.FloatTensor(act).to(self.device) + rew_t = torch.FloatTensor(rew).unsqueeze(1).to(self.device) + next_obs_t = torch.FloatTensor(next_obs).to(self.device) + dones_t = torch.FloatTensor(dones).unsqueeze(1).to(self.device) + + # 1. Critic update (1. Critic 更新) + with torch.no_grad(): + next_act = self.actor_target(next_obs_t) + target_q = rew_t + self.gamma * (1 - dones_t) * self.critic_target(next_obs_t, next_act) + + current_q = self.critic(obs_t, act_t) + critic_loss = F.mse_loss(current_q, target_q) + self.critic_optimizer.zero_grad() + critic_loss.backward() + self.critic_optimizer.step() + + # 2. Actor update (2. Actor 更新) + new_act = self.actor(obs_t) + actor_loss = -self.critic(obs_t, new_act).mean() + self.actor_optimizer.zero_grad() + actor_loss.backward() + self.actor_optimizer.step() + + # 3. Soft update targets (3. 目标网络软更新) + for target, source in [ + (self.critic_target, self.critic), + (self.actor_target, self.actor), + ]: + for tp, sp in zip(target.parameters(), source.parameters()): + tp.data.copy_(self.tau * sp.data + (1.0 - self.tau) * tp.data) + + return {'actor_loss': actor_loss.item(), 'critic_loss': critic_loss.item()} + + def save(self, path): + """Save models.""" + os.makedirs(path, exist_ok=True) + torch.save(self.actor.state_dict(), os.path.join(path, "actor.pth")) + torch.save(self.critic.state_dict(), os.path.join(path, "critic.pth")) + + def load(self, path): + """Load models.""" + self.actor.load_state_dict(torch.load(os.path.join(path, "actor.pth"), map_location=self.device)) + self.critic.load_state_dict(torch.load(os.path.join(path, "critic.pth"), map_location=self.device)) + self.actor_target.load_state_dict(self.actor.state_dict()) + self.critic_target.load_state_dict(self.critic.state_dict()) diff --git a/code/baselines/single_dqn.py b/code/baselines/single_dqn.py new file mode 100644 index 0000000..2f0ab60 --- /dev/null +++ b/code/baselines/single_dqn.py @@ -0,0 +1,296 @@ +import os +import random +from collections import deque +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +""" +Baseline: SingleAgentDQN (单智能体 DQN 基线) +===================================== +Purpose (non-MARL baseline): +- This baseline represents a traditional single-agent approach to the resource allocation problem. +- It uses a centralized DQN that controls both groups by discretizing the continuous action space. +- 目的（非多智能体基线）：该基线代表了解决资源分配问题的传统单智能体方法。它使用中心化 DQN，通过对连续动作空间进行离散化，同时控制两个用户组。 + +Difference from Co-MADDPG: +1. Algorithm Class: Non-MARL (DQN) vs MARL (Co-MADDPG). +2. Action Space: Discrete (48 actions) vs Continuous. +3. Architecture: Centralized control vs Decentralized execution with CTDE. +4. Exploration: Epsilon-greedy vs OU Noise. +5. 与 Co-MADDPG 的区别： + - 算法类别：非多智能体 (DQN) vs 多智能体 (Co-MADDPG)。 + - 动作空间：离散（48 种动作组合） vs 连续。 + - 架构：中心化控制 vs CTDE 架构下的分布式执行。 + - 探索机制：ε-greedy vs OU 噪声。 + +Contribution: +- Contributes to performance tables showing the limitations of discretization and centralized control in complex multi-user scenarios. +- 贡献：用于性能表，展示在复杂多用户场景下，动作离散化和中心化控制的局限性。 +""" + +# ---- Discrete action mapping (离散动作映射) ---- +# 4 levels for subcarrier fraction, 4 for power fraction, 3 for m_param +# 子载波比例 4 级，功率比例 4 级，m 参数 3 级 +N_SUB_LEVELS = [0.25, 0.5, 0.75, 1.0] +P_FRAC_LEVELS = [0.25, 0.5, 0.75, 1.0] +M_PARAM_LEVELS = [0.33, 0.66, 1.0] +NUM_ACTIONS = len(N_SUB_LEVELS) * len(P_FRAC_LEVELS) * len(M_PARAM_LEVELS) # 48 combinations + +# Build lookup table: index -> (n_sub_frac, p_frac, m_param) +# 构建查找表：索引 -> (子载波比例, 功率比例, m 参数) +_ACTION_TABLE = [] +for n in N_SUB_LEVELS: + for p in P_FRAC_LEVELS: + for m in M_PARAM_LEVELS: + _ACTION_TABLE.append(np.array([n, p, m], dtype=np.float32)) + + +class DQNNet(nn.Module): + """ + Simple Fully Connected Q-network. + 简单的全连接 Q 网络。 + """ + def __init__(self, state_dim, num_actions): + super().__init__() + self.net = nn.Sequential( + nn.Linear(state_dim, 256), + nn.ReLU(), + nn.Linear(256, 256), + nn.ReLU(), + nn.Linear(256, num_actions), + ) + + def forward(self, x): + """Map state to Q-values for each discrete action.""" + return self.net(x) + + +class DQNReplayBuffer: + """ + Wrapper buffer for SingleAgentDQN. + 单智能体 DQN 的封装重放池。 + + Accepts the multi-agent 9-argument signature but stores transitions suitable for DQN. + 接收多智能体的 9 参数签名，但内部存储适合 DQN 的转换数据。 + """ + def __init__(self, capacity): + self.buffer = deque(maxlen=capacity) + self._last_action_s_idx = 0 + self._last_action_b_idx = 0 + + def set_last_actions(self, idx_s, idx_b): + """Store the discrete action indices used.""" + self._last_action_s_idx = idx_s + self._last_action_b_idx = idx_b + + def push(self, obs_s, obs_b, act_s, act_b, rew_s, rew_b, + next_obs_s, next_obs_b, done=False): + """ + Store multi-agent step as a single-agent transition. + 将多智能体步骤作为单智能体转换存储。 + """ + # Concatenate observations for centralized state + # 拼接观察值以形成中心化状态 + state = np.concatenate([np.asarray(obs_s, dtype=np.float32), + np.asarray(obs_b, dtype=np.float32)]) + next_state = np.concatenate([np.asarray(next_obs_s, dtype=np.float32), + np.asarray(next_obs_b, dtype=np.float32)]) + # Average rewards for single-agent scalar reward + # 对奖励求平均以获得单智能体标量奖励 + reward = 0.5 * (float(rew_s) + float(rew_b)) + self.buffer.append((state, self._last_action_s_idx, self._last_action_b_idx, + reward, next_state, float(done))) + + def sample(self, batch_size): + """Sample a batch of transitions.""" + batch = random.sample(self.buffer, batch_size) + states, a_s, a_b, rewards, next_states, dones = zip(*batch) + return (np.array(states), np.array(a_s), np.array(a_b), + np.array(rewards, dtype=np.float32), + np.array(next_states), np.array(dones, dtype=np.float32)) + + def __len__(self): + return len(self.buffer) + + +class SingleAgentDQN: + """ + SingleAgentDQN algorithm implementation. + 单智能体 DQN 算法实现。 + """ + def __init__(self, config): + # Initialize configuration and device + # 初始化配置和设备 + self.config = config + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Hyperparameters + # 超参数 + self.gamma = config['training']['gamma'] + self.batch_size = config['training']['batch_size'] + self.tau = config['training']['tau'] + + # Dimensions: Concentated state + # 维度：拼接后的状态 + self.obs_dim = config['env']['num_subcarriers'] + 4 + self.state_dim = self.obs_dim * 2 + self.num_actions = NUM_ACTIONS + + # Two DQN heads: one for semantic (s) actions, one for traditional (b) actions + # 两个 DQN 头：一个用于语义动作 (s)，一个用于传统动作 (b) + self.q_net_s = DQNNet(self.state_dim, self.num_actions).to(self.device) + self.q_net_b = DQNNet(self.state_dim, self.num_actions).to(self.device) + self.q_target_s = DQNNet(self.state_dim, self.num_actions).to(self.device) + self.q_target_b = DQNNet(self.state_dim, self.num_actions).to(self.device) + self.q_target_s.load_state_dict(self.q_net_s.state_dict()) + self.q_target_b.load_state_dict(self.q_net_b.state_dict()) + + # Optimizers + # 优化器 + lr = config['training'].get('actor_lr', 1e-4) + self.optimizer_s = torch.optim.Adam(self.q_net_s.parameters(), lr=lr) + self.optimizer_b = torch.optim.Adam(self.q_net_b.parameters(), lr=lr) + + # Epsilon-greedy exploration parameters + # ε-greedy 探索参数 + self.epsilon = 1.0 + self.epsilon_min = 0.01 + self.epsilon_decay_episodes = 3000 + + # Specialized Replay Buffer + # 专用的重放池 + self.replay_buffer = DQNReplayBuffer(config['training']['buffer_capacity']) + + # Discrete action index tracking + # 离散动作索引追踪 + self._last_action_s_idx = 0 + self._last_action_b_idx = 0 + + # EpsilonAdapter: Hack to allow epsilon decay via train.py's existing loop + # EpsilonAdapter：用于通过 train.py 现有循环触发 ε 衰减的技巧 + self.noise_s = type('EpsilonAdapter', (), { + 'decay_sigma': lambda _, ep: self._decay_epsilon(ep) + })() + + def select_action(self, obs_s, obs_b, explore=True): + """ + Select discrete actions using epsilon-greedy policy. + 使用 ε-greedy 策略选择离散动作。 + """ + state = np.concatenate([obs_s, obs_b]).astype(np.float32) + state_t = torch.FloatTensor(state).unsqueeze(0).to(self.device) + + if explore and random.random() < self.epsilon: + # Random exploration + # 随机探索 + idx_s = random.randrange(self.num_actions) + idx_b = random.randrange(self.num_actions) + else: + # Exploit learned Q-values + # 利用已学习的 Q 值 + self.q_net_s.eval() + self.q_net_b.eval() + with torch.no_grad(): + q_s = self.q_net_s(state_t) + q_b = self.q_net_b(state_t) + self.q_net_s.train() + self.q_net_b.train() + idx_s = q_s.argmax(dim=1).item() + idx_b = q_b.argmax(dim=1).item() + + # Update last indices for the buffer push + # 更新用于存入重放池的最后索引 + self._last_action_s_idx = idx_s + self._last_action_b_idx = idx_b + self.replay_buffer.set_last_actions(idx_s, idx_b) + + # Return continuous actions from lookup table + # 从查找表中返回对应的连续动作 + return _ACTION_TABLE[idx_s].copy(), _ACTION_TABLE[idx_b].copy() + + def compute_rewards(self, qoe_s, qoe_b, qoe_sys): + """ + Compute scalar reward for single agent. + 为单智能体计算标量奖励。 + + Formula: r = 0.5 * (qoe_s + qoe_b) + 公式说明：由于是单智能体控制全局，奖励取两组用户 QoE 的均值。 + """ + lam = 0.5 + r = 0.5 * (qoe_s + qoe_b) + return r, r, lam + + def update(self): + """ + Update the Q-networks. + 更新 Q 网络。 + """ + if len(self.replay_buffer) < self.batch_size: + return None + + # Sample batch + # 采样批量数据 + states, a_s, a_b, rewards, next_states, dones = \ + self.replay_buffer.sample(self.batch_size) + + # To tensors + # 转换为张量 + states_t = torch.FloatTensor(states).to(self.device) + next_states_t = torch.FloatTensor(next_states).to(self.device) + rewards_t = torch.FloatTensor(rewards).unsqueeze(1).to(self.device) + dones_t = torch.FloatTensor(dones).unsqueeze(1).to(self.device) + a_s_t = torch.LongTensor(a_s).unsqueeze(1).to(self.device) + a_b_t = torch.LongTensor(a_b).unsqueeze(1).to(self.device) + + # 1. Update Semantic Head (1. 更新语义分支) + q_values_s = self.q_net_s(states_t).gather(1, a_s_t) + with torch.no_grad(): + next_q_s = self.q_target_s(next_states_t).max(1, keepdim=True)[0] + target_s = rewards_t + self.gamma * (1 - dones_t) * next_q_s + loss_s = F.mse_loss(q_values_s, target_s) + self.optimizer_s.zero_grad() + loss_s.backward() + self.optimizer_s.step() + + # 2. Update Traditional Head (2. 更新传统分支) + q_values_b = self.q_net_b(states_t).gather(1, a_b_t) + with torch.no_grad(): + next_q_b = self.q_target_b(next_states_t).max(1, keepdim=True)[0] + target_b = rewards_t + self.gamma * (1 - dones_t) * next_q_b + loss_b = F.mse_loss(q_values_b, target_b) + self.optimizer_b.zero_grad() + loss_b.backward() + self.optimizer_b.step() + + # 3. Soft update target networks (3. 目标网络软更新) + for target, source in [ + (self.q_target_s, self.q_net_s), + (self.q_target_b, self.q_net_b), + ]: + for tp, sp in zip(target.parameters(), source.parameters()): + tp.data.copy_(self.tau * sp.data + (1.0 - self.tau) * tp.data) + + return {'loss_s': loss_s.item(), 'loss_b': loss_b.item()} + + def _decay_epsilon(self, episode): + """ + Decay epsilon over episodes. + 随训练轮数衰减 ε。 + """ + frac = min(1.0, episode / max(1, self.epsilon_decay_episodes)) + self.epsilon = self.epsilon + frac * (self.epsilon_min - self.epsilon) + + def save(self, path): + """Save Q-nets.""" + os.makedirs(path, exist_ok=True) + torch.save(self.q_net_s.state_dict(), os.path.join(path, "q_net_s.pth")) + torch.save(self.q_net_b.state_dict(), os.path.join(path, "q_net_b.pth")) + + def load(self, path): + """Load Q-nets.""" + self.q_net_s.load_state_dict(torch.load(os.path.join(path, "q_net_s.pth"), map_location=self.device)) + self.q_net_b.load_state_dict(torch.load(os.path.join(path, "q_net_b.pth"), map_location=self.device)) + self.q_target_s.load_state_dict(self.q_net_s.state_dict()) + self.q_target_b.load_state_dict(self.q_net_b.state_dict()) diff --git a/code/configs/__init__.py b/code/configs/__init__.py new file mode 100644 index 0000000..8cfbec5 --- /dev/null +++ b/code/configs/__init__.py @@ -0,0 +1 @@ +# configs/__init__.py diff --git a/code/configs/default.yaml b/code/configs/default.yaml new file mode 100644 index 0000000..87cdd42 --- /dev/null +++ b/code/configs/default.yaml @@ -0,0 +1,81 @@ +# ============================================================================= +# Co-MADDPG Wireless Resource Allocation — Default Configuration +# ============================================================================= +# All hyperparameters follow the paper's specifications for semantic-aware +# cooperative multi-agent resource allocation in OFDMA systems. +# ============================================================================= + +env: + # OFDMA system parameters + num_subcarriers: 64 # N: total number of OFDM subcarriers + bandwidth: 10.0e+6 # B: total system bandwidth (Hz) + subcarrier_spacing: 156250.0 # Δf: subcarrier spacing (Hz), B/N + max_power: 1.0 # P_max: maximum transmit power per user (W) + noise_psd: -174 # N0: noise power spectral density (dBm/Hz) + carrier_freq: 3.5 # f_c: carrier frequency (GHz) + + # Cell geometry + min_distance: 50 # d_min: minimum BS-user distance (m) + max_distance: 500 # d_max: maximum BS-user distance (m) + + # User configuration + num_semantic_users: 3 # K_s: number of semantic communication users + num_traditional_users: 3 # K_b: number of traditional bit-rate users + + # QoS constraints + min_rate_req: 5.0e+5 # R_min: minimum rate requirement for traditional users (bps) + + # Semantic compression ratio bounds + rho_max: 1.0 # ρ_max: maximum compression ratio (no compression) + rho_min: 0.05 # ρ_min: minimum compression ratio + + # QoE weighting factors + w1: 0.7 # w1: weight for semantic similarity (SSIM) + w2: 0.3 # w2: weight for compression efficiency + +training: + # Episode configuration + max_episodes: 5000 # total training episodes + max_steps: 200 # maximum steps per episode + + # Replay buffer and sampling + batch_size: 256 # mini-batch size for gradient updates + buffer_capacity: 100000 # replay buffer capacity + + # Learning rates + actor_lr: 1.0e-4 # actor network learning rate + critic_lr: 3.0e-4 # critic network learning rate + + # Discount and soft-update + gamma: 0.95 # discount factor γ + tau: 0.01 # soft target update rate τ + + # Ornstein-Uhlenbeck exploration noise + ou_sigma_init: 0.2 # initial noise standard deviation + ou_sigma_min: 0.01 # minimum noise standard deviation + ou_theta: 0.15 # OU mean-reversion rate θ + + # Cooperative mechanism parameters + beta: 5.0 # β: cooperation benefit scaling factor + q_threshold: 0.6 # Q-value threshold for cooperation mode switch + update_interval: 5 # target network update interval (episodes) + + # Reproducibility + seed: 42 + +network: + # Actor network hidden layer dimensions + actor_hidden: [256, 256, 128] + + # Critic network hidden layer dimensions + critic_hidden: [512, 512, 256] + +reward: + # Cooperative mode reward weights + coop_self: 0.5 # α_self: weight on own reward (cooperative) + coop_other: 0.3 # α_other: weight on other agents' reward + coop_sys: 0.2 # α_sys: weight on system-level reward + + # Competitive mode reward weights + comp_self: 0.8 # α_self: weight on own reward (competitive) + comp_sys: 0.2 # α_sys: weight on system-level reward diff --git a/code/envs/__init__.py b/code/envs/__init__.py new file mode 100644 index 0000000..db75b1f --- /dev/null +++ b/code/envs/__init__.py @@ -0,0 +1,6 @@ +"""Environment modules for Co-MADDPG wireless resource allocation.""" + +from .channel_model import ChannelModel +from .semantic_module import SemanticModule + +__all__ = ["ChannelModel", "SemanticModule"] diff --git a/code/envs/channel_model.py b/code/envs/channel_model.py new file mode 100644 index 0000000..0b7abef --- /dev/null +++ b/code/envs/channel_model.py @@ -0,0 +1,197 @@ +""" +无线资源分配信道模型 / Channel model for OFDMA wireless resource allocation. + +该模块实现了 3GPP 风格的路径损耗模型和多用户 OFDMA 下行链路系统的复信道增益生成。 +所有公式遵循论文中的公式 (5)-(8)。 +This module implements the 3GPP-style path loss model and complex channel gain generation +for a multi-user OFDMA downlink system. All formulas follow the paper's equations (5)–(8). + +作者/Author: Sisyphus-Junior +日期/Date: 2026-02-28 +论文引用/Paper Reference: Co-MADDPG based Resource Allocation for Semantic Communication +依赖/Dependencies: numpy +""" + +import numpy as np + + +class ChannelModel: + """ + 多用户 OFDMA 系统的频率选择性信道模型。 + Frequency-selective channel model for multi-user OFDMA systems. + + 生成包含距离相关路径损耗和瑞利衰落的每子载波复信道增益，并计算每子载波的信噪比 (SNR)。 + Generates per-subcarrier complex channel gains incorporating distance-dependent + path loss with Rayleigh fading, and computes per-subcarrier SNR values. + + Parameters + ---------- + config : dict + 完整的配置字典（必须包含 "env" 部分，且具有 carrier_freq, noise_psd 和 subcarrier_spacing 键）。 + Full configuration dictionary (must contain an "env" section with keys + "carrier_freq", "noise_psd", and "subcarrier_spacing"). + """ + + def __init__(self, config: dict) -> None: + # 初始化环境配置 / Initialize environment configurations + self.config = config + env = config["env"] + # 载波频率 (GHz) / Carrier frequency in GHz + self._carrier_freq_ghz: float = env["carrier_freq"] + # 噪声功率谱密度 (dBm/Hz) / Noise power spectral density in dBm/Hz + self._noise_psd_dbm: float = env["noise_psd"] + # 子载波间隔 (Hz) / Subcarrier spacing in Hz + self._subcarrier_spacing: float = env["subcarrier_spacing"] + + # ------------------------------------------------------------------ + # 路径损耗 / Path loss + # ------------------------------------------------------------------ + + def path_loss(self, distance: float) -> float: + """ + 计算与距离相关的路径损耗 (dB)。 + Compute distance-dependent path loss in dB. + + 使用 3GPP Urban Micro (UMi) NLOS 模型 (公式 5): + Uses the 3GPP Urban Micro (UMi) NLOS model (Eq. 5): + + PL(d) = 36.7 * log10(d) + 22.7 + 26 * log10(fc) + + 其中 d 的单位为米，fc 的单位为 GHz。 + where *d* is in metres and *fc* is in GHz. + + Parameters + ---------- + distance : float or np.ndarray + 收发机之间的距离，单位为米。 + Transmitter–receiver distance(s) in metres. + + Returns + ------- + float or np.ndarray + 路径损耗值，单位为 dB。 + Path loss value(s) in dB. + """ + fc = self._carrier_freq_ghz + # 应用 3GPP UMi NLOS 公式 / Apply 3GPP UMi NLOS formula - Eq.(5) + return 36.7 * np.log10(distance) + 22.7 + 26.0 * np.log10(fc) + + # ------------------------------------------------------------------ + # 信道生成 / Channel generation + # ------------------------------------------------------------------ + + def generate_channel( + self, distances: np.ndarray, num_subcarriers: int + ) -> np.ndarray: + """ + 生成所有用户和子载波的复信道增益。 + Generate complex channel gains for all users and subcarriers. + + 每个元素 h_{k,n} 服从复高斯分布 CN(0, 10^{-PL/10}) (公式 6)。 + 即：独立循环对称复高斯分布，其方差等于线性尺度的逆路径损耗。 + Each element h_{k,n} is drawn from CN(0, 10^{-PL/10}) (Eq. 6), i.e. + independent circularly-symmetric complex Gaussian with variance + equal to the linear-scale inverse path loss. + + Parameters + ---------- + distances : array_like, shape (K,) + 每个用户距离基站的距离（米）。 + Distance of each user from the base station (metres). + num_subcarriers : int + OFDM 子载波数量 N。 + Number of OFDM subcarriers *N*. + + Returns + ------- + np.ndarray, shape (K, N) + 复信道增益矩阵。 + Complex channel gain matrix. + """ + distances = np.asarray(distances, dtype=np.float64) + K = len(distances) + N = num_subcarriers + + # 每用户路径损耗 -> 线性尺度信道方差 / Per-user path loss -> linear-scale channel variance + pl_db = self.path_loss(distances) # (K,) + # 方差 = 10^(-PL/10) / Variance = 10^(-PL/10) - Eq.(6) + variance = 10.0 ** (-pl_db / 10.0) # (K,) + variance = variance.reshape(K, 1) # (K, 1) 用于广播 / for broadcasting + + # 复高斯：每个分量服从 N(0, var/2) / Complex Gaussian: each component ~ N(0, var/2) + std = np.sqrt(variance / 2.0) + # 生成实部和虚部 / Generate real and imaginary parts + real_part = np.random.randn(K, N) * std + imag_part = np.random.randn(K, N) * std + + # 返回复增益 / Return complex gains + return real_part + 1j * imag_part + + # ------------------------------------------------------------------ + # SNR 计算 / SNR computation + # ------------------------------------------------------------------ + + def compute_snr( + self, + channel_gains: np.ndarray, + power_alloc: np.ndarray, + noise_power: float, + ) -> np.ndarray: + """ + 计算每个用户的每子载波信噪比 (SNR)。 + Compute per-subcarrier SNR for every user. + + γ_{k,n} = p_{k,n} * |h_{k,n}|² / σ² (公式 8) + γ_{k,n} = p_{k,n} · |h_{k,n}|² / σ² (Eq. 8) + + Parameters + ---------- + channel_gains : np.ndarray, shape (K, N) + 复信道增益矩阵。 + Complex channel gain matrix. + power_alloc : np.ndarray, shape (K, N) + 每个用户在每个子载波上分配的功率（瓦特）。 + Power allocated by each user on each subcarrier (Watts). + noise_power : float + 每子载波的噪声功率 σ²（瓦特）。 + Noise power σ² per subcarrier (Watts). + + Returns + ------- + np.ndarray, shape (K, N) + SNR 值（线性尺度）。 + SNR values (linear scale). + """ + # 计算 SNR = 功率 * 增益平方 / 噪声 / Compute SNR = Power * Gain^2 / Noise - Eq.(8) + return power_alloc * (np.abs(channel_gains) ** 2) / noise_power + + # ------------------------------------------------------------------ + # 噪声功率属性 / Noise power property + # ------------------------------------------------------------------ + + @property + def noise_power(self) -> float: + """ + 每子载波的热噪声功率 (瓦特)。 + Thermal noise power per subcarrier (Watts). + + σ² = N₀ * Δf (公式 7) + σ² = N₀ · Δf (Eq. 7) + + 其中 N₀ 是从 dBm/Hz 转换为线性 (W/Hz) 的噪声功率谱密度： + where N₀ is the noise PSD converted from dBm/Hz to linear (W/Hz): + + N₀_linear = 10^((N₀_dBm - 30) / 10) + + Returns + ------- + float + 噪声功率（瓦特）。 + Noise power in Watts. + """ + n0_dbm = self._noise_psd_dbm + delta_f = self._subcarrier_spacing + # 转换为线性功率谱密度 / Convert to linear PSD - Eq.(7) + n0_linear = 10.0 ** ((n0_dbm - 30.0) / 10.0) + # 计算总噪声功率 / Compute total noise power + return n0_linear * delta_f diff --git a/code/envs/semantic_module.py b/code/envs/semantic_module.py new file mode 100644 index 0000000..b54bac3 --- /dev/null +++ b/code/envs/semantic_module.py @@ -0,0 +1,156 @@ +""" +语义通信模块 / Semantic communication module for Co-MADDPG. + +实现基于 DeepSC 经验曲线的语义相似度 (SSim) 计算，以及语义通信用户的 QoE 计算。 +Implements semantic similarity (SSim) computation based on empirical DeepSC curves, +and QoE calculation for semantic communication users. + +作者/Author: Sisyphus-Junior +日期/Date: 2026-02-28 +论文引用/Paper Reference: Co-MADDPG based Resource Allocation for Semantic Communication +依赖/Dependencies: numpy +""" + +import numpy as np + + +class SemanticModule: + """ + 语义通信质量模块。 + Semantic communication quality module. + + 根据平均 SNR 和压缩率，使用受 DeepSC 文献启发的经验拟合曲线计算语义相似度指数 (SSim)。 + Computes semantic similarity index (SSim) from average SNR and compression + ratio, using empirical fitting curves inspired by DeepSC literature. + + Parameters + ---------- + config : dict + 完整的配置字典（必须包含 "env" 部分，且具有 rho_max, rho_min, w1, w2 键）。 + Full configuration dictionary (must contain an "env" section + with keys "rho_max", "rho_min", "w1", "w2"). + """ + + def __init__(self, config: dict) -> None: + # 初始化语义参数 / Initialize semantic parameters + self.config = config + env = config["env"] + # 最大压缩率 / Maximum compression ratio ρ_max + self.rho_max = env.get("rho_max", 1.0) + # 最小压缩率 / Minimum compression ratio ρ_min + self.rho_min = env.get("rho_min", 0.05) + # QoE 权重 1 / QoE Weight w1 (SSim weight) + self.w1 = env.get("w1", 0.7) + # QoE 权重 2 / QoE Weight w2 (Resource efficiency weight) + self.w2 = env.get("w2", 0.3) + + @staticmethod + def _a(rho: float) -> float: + """经验曲线参数 a(ρ) = 0.8 / (ρ + 0.1)。 / Empirical curve parameter a(ρ) = 0.8 / (ρ + 0.1).""" + return 0.8 / (rho + 0.1) + + @staticmethod + def _b(rho: float) -> float: + """经验曲线参数 b(ρ) = 0.6 + 0.2 * ρ。 / Empirical curve parameter b(ρ) = 0.6 + 0.2 * ρ.""" + return 0.6 + 0.2 * rho + + def compute_ssim(self, avg_snr, rho: float): + """ + 计算语义相似度指数 (SSim)。 + Compute semantic similarity index (SSim). + + φ(γ̄, ρ) = 1 - exp(-a(ρ) * γ̄^{b(ρ)}) (公式参考 SSim 章节) + φ(γ̄, ρ) = 1 - exp(-a(ρ) * γ̄^{b(ρ)}) (Refer to SSim section in paper) + + Parameters + ---------- + avg_snr : float or np.ndarray + 线性尺度的平均 SNR（非 dB）。 + Average SNR in linear scale (not dB). + rho : float + 压缩率 ρ ∈ [ρ_min, ρ_max]。 + Compression ratio ρ ∈ [ρ_min, ρ_max]. + + Returns + ------- + float or np.ndarray + [0, 1] 范围内的语义相似度。 + Semantic similarity in [0, 1]. + """ + # 防止 SNR 过小导致数值错误 / Avoid numerical errors with small SNR + avg_snr = np.maximum(avg_snr, 1e-10) + # 获取经验参数 a 和 b / Get empirical parameters a and b + a = self._a(rho) + b = self._b(rho) + # 计算 SSim 公式 / Compute SSim formula + return 1.0 - np.exp(-a * np.power(avg_snr, b)) + + def compute_avg_snr(self, snr_per_subcarrier: np.ndarray, + allocation_mask: np.ndarray) -> float: + """ + 计算已分配子载波上的平均 SNR。 + Compute average SNR over allocated subcarriers. + + Parameters + ---------- + snr_per_subcarrier : np.ndarray + 所有子载波的 SNR 值（线性尺度）。 + SNR values for all subcarriers (linear scale). + allocation_mask : np.ndarray + 指示已分配子载波的二进制掩码。 + Binary mask indicating allocated subcarriers. + + Returns + ------- + float + 已分配子载波的平均 SNR；若未分配则返回 0。 + Mean SNR over allocated subcarriers; 0 if none allocated. + """ + # 提取已分配子载波的 SNR / Extract SNR for allocated subcarriers + allocated = snr_per_subcarrier[allocation_mask > 0] + # 如果没有子载波被分配 / If no subcarriers are allocated + if len(allocated) == 0: + return 0.0 + # 返回平均值 / Return mean value + return float(np.mean(allocated)) + + def compute_semantic_qoe(self, ssim: float, rho: float, + w1: float = None, w2: float = None, + rho_max: float = None) -> float: + """ + 计算语义通信用户的 QoE。 + Compute QoE for a semantic communication user. + + QoE_s = w1 * SSim + w2 * (1 - ρ / ρ_max) (公式参考 QoE_s) + QoE_s = w1 * SSim + w2 * (1 - ρ / ρ_max) (Refer to QoE_s formula) + + Parameters + ---------- + ssim : float + [0, 1] 范围内的语义相似度指数。 + Semantic similarity index in [0, 1]. + rho : float + 使用的压缩率。 + Compression ratio used. + w1, w2 : float, optional + 权重（默认为配置中的实例值）。 + Weights (defaults to instance values from config). + rho_max : float, optional + 最大压缩率（默认为配置中的值）。 + Maximum compression ratio (default from config). + + Returns + ------- + float + [0, 1] 范围内的 QoE 值。 + QoE value in [0, 1]. + """ + # 使用默认值或输入值 / Use default or input values + if w1 is None: + w1 = self.w1 + if w2 is None: + w2 = self.w2 + if rho_max is None: + rho_max = self.rho_max + # 计算语义 QoE / Calculate semantic QoE + return float(w1 * ssim + w2 * (1.0 - rho / rho_max)) diff --git a/code/envs/wireless_env.py b/code/envs/wireless_env.py new file mode 100644 index 0000000..b8062d4 --- /dev/null +++ b/code/envs/wireless_env.py @@ -0,0 +1,336 @@ +""" +无线资源分配环境 / Main Gym-like environment for wireless resource allocation. + +该模块实现了一个用于语义和传统用户共存系统的无线资源分配环境。 +它通过 Gym 风格的 reset/step 接口，处理子载波分配、功率控制和压缩率优化。 +This module implements a wireless resource allocation environment for systems +with coexisting semantic and traditional users. It handles subcarrier allocation, +power control, and compression ratio optimization via a Gym-like reset/step interface. + +作者/Author: Sisyphus-Junior +日期/Date: 2026-02-28 +论文引用/Paper Reference: Co-MADDPG based Resource Allocation for Semantic Communication +依赖/Dependencies: numpy, envs.channel_model, envs.semantic_module +""" + +import numpy as np +from envs.channel_model import ChannelModel +from envs.semantic_module import SemanticModule + +class WirelessEnv: + """ + 语义与传统通信共存环境。 + Wireless environment with semantic and traditional communication. + + 管理信道状态、执行动作并计算系统范围内的 QoE。 + Manages channel states, executes actions, and computes system-wide QoE. + + Parameters + ---------- + config : dict + 包含 'env' 和 'training' 部分的配置字典。 + Configuration dictionary containing 'env' and 'training' sections. + """ + def __init__(self, config): + # 提取环境和训练配置 / Extract environment and training configs + env_config = config['env'] + train_config = config['training'] + + # 核心系统参数 / Core system parameters + self.N = env_config['num_subcarriers'] # 子载波数量 N / Number of subcarriers + self.K_s = env_config['num_semantic_users'] # 语义用户数 / Number of semantic users + self.K_b = env_config['num_traditional_users'] # 传统用户数 / Number of traditional users + self.K = self.K_s + self.K_b # 总用户数 / Total number of users + + # 物理层参数 / Physical layer parameters + self.P_max = env_config['max_power'] # 最大总发射功率 / Maximum total transmit power + self.R_req = env_config['min_rate_req'] # 传统用户最小速率需求 / Min rate requirement for traditional users + self.delta_f = env_config['subcarrier_spacing'] # 子载波间隔 / Subcarrier spacing + self.rho_min = env_config['rho_min'] # 最小压缩率 / Minimum compression ratio + self.rho_max = env_config['rho_max'] # 最大压缩率 / Maximum compression ratio + self.w1 = env_config['w1'] # 语义 QoE 权重 1 / Semantic QoE weight 1 + self.w2 = env_config['w2'] # 语义 QoE 权重 2 / Semantic QoE weight 2 + + # 距离限制 / Distance limits + self.min_d = env_config.get('min_distance', 50.0) + self.max_d = env_config.get('max_distance', 500.0) + + # 训练步数控制 / Training step control + self.max_steps = train_config['max_steps'] + self.step_count = 0 + + # 初始化模型 / Initialize models + self.channel_model = ChannelModel(config) + self.semantic_module = SemanticModule(config) + + # 初始状态变量 / Initial state variables + self.distances = np.zeros(self.K) # 用户距离 / User distances + self.channel_gains = np.zeros((self.K, self.N), dtype=complex) # 复信道增益 / Complex channel gains + self.content_sensitivity = 0.5 # 内容敏感度 / Content sensitivity + self.business_priority = 0.5 # 业务优先级 / Business priority + self.load_s = 0.5 # 语义流量负载 / Semantic traffic load + self.load_b = 0.5 # 传统流量负载 / Traditional traffic load + self.alloc_s = 0.0 # 语义子载波分配比例 / Semantic subcarrier allocation fraction + self.alloc_b = 0.0 # 传统子载波分配比例 / Traditional subcarrier allocation fraction + self.qoe_avg_s = 0.0 # 语义平均 QoE / Rolling average semantic QoE + self.qoe_avg_b = 0.0 # 传统平均 QoE / Rolling average traditional QoE + + @property + def obs_dim(self): + """观察维度: 子载波 (N) + 4 个额外特征。 / Observation dimension: Subcarriers (N) + 4 extra features.""" + return self.N + 4 + + @property + def act_dim(self): + """动作维度: 子载波比例, 功率比例, [语义: 压缩率]。 / Action dimension: Subcarrier fraction, Power fraction, [Semantic: Compression ratio].""" + return 3 + + def reset(self): + """ + 重置环境状态。 + Reset environment state. + + Returns + ------- + tuple + (语义智能体观察, 传统智能体观察)。 + (semantic_observation, traditional_observation). + """ + # 在 [min_distance, max_distance] 内随机分配用户距离 / Random user distances in [min_distance, max_distance] + self.distances = np.random.uniform(self.min_d, self.max_d, size=self.K) + + # 生成信道 (形状: K x N 复数) / Generate channel (shape: K x N complex) - Eq.(6) + self.channel_gains = self.channel_model.generate_channel(self.distances, self.N) + self.step_count = 0 + + # 随机设置观察参数 / Random params for observation + self.content_sensitivity = np.random.uniform(0.3, 0.8) + self.business_priority = np.random.uniform(0.3, 0.8) + self.load_s = np.random.uniform(0.2, 0.8) + self.load_b = np.random.uniform(0.2, 0.8) + + # 重置分配比例和移动平均值 / Reset allocations and moving averages + self.alloc_s = 0.0 + self.alloc_b = 0.0 + self.qoe_avg_s = 0.0 + self.qoe_avg_b = 0.0 + + # 获取初始观察 / Get initial observations + obs_s = self._get_observation('semantic') + obs_b = self._get_observation('traditional') + return obs_s, obs_b + + def _get_observation(self, agent_type): + """ + 构造智能体的观察向量。 + Construct observation vector for agents. + + Parameters + ---------- + agent_type : str + 'semantic' 或 'traditional'。 + 'semantic' or 'traditional'. + + Returns + ------- + np.ndarray + 归一化后的观察向量。 + Normalized observation vector. + """ + if agent_type == 'semantic': + # 语义用户索引范围 / Semantic user indices range + user_indices = range(self.K_b, self.K) + if len(user_indices) > 0: + # 计算平均信道增益平方 (功率) / Mean channel power + channel_power = np.mean(np.abs(self.channel_gains[user_indices])**2, axis=0) + else: + channel_power = np.zeros(self.N) + + # 归一化信道功率 / Normalize channel power + channel_norm = channel_power / (np.max(channel_power) + 1e-10) + # 拼接额外特征 / Concatenate extra features + obs = np.concatenate([channel_norm, + [self.qoe_avg_s, self.content_sensitivity, self.alloc_s, self.load_s]]) + + else: # 传统 / traditional + # 传统用户索引范围 / Traditional user indices range + user_indices = range(0, self.K_b) + if len(user_indices) > 0: + # 计算平均信道功率 / Mean channel power + channel_power = np.mean(np.abs(self.channel_gains[user_indices])**2, axis=0) + else: + channel_power = np.zeros(self.N) + + # 归一化信道功率 / Normalize channel power + channel_norm = channel_power / (np.max(channel_power) + 1e-10) + # 拼接额外特征 / Concatenate extra features + obs = np.concatenate([channel_norm, + [self.qoe_avg_b, self.business_priority, self.alloc_b, self.load_b]]) + + # 返回 32位浮点型观察 / Return float32 observation + return obs.astype(np.float32) + + def step(self, action_s, action_b): + """ + 执行一个时间步。 + Execute a single environment step. + + Parameters + ---------- + action_s : np.ndarray + 语义智能体动作 [子载波比例, 功率比例, 压缩率]。 + Semantic agent action [sub_fraction, power_fraction, compression_ratio]. + action_b : np.ndarray + 传统智能体动作 [子载波比例, 功率比例, 冗余参数]。 + Traditional agent action [sub_fraction, power_fraction, redundant_param]. + + Returns + ------- + tuple + (obs_s, obs_b, reward_s, reward_b, done, info). + """ + self.step_count += 1 + + # 1. 解码动作 / Decode actions + # 计算子载波分配数量 / Compute number of subcarriers + n_sub_s = max(1, int(round(action_s[0] * self.N))) + n_sub_b = max(1, int(round(action_b[0] * self.N))) + + # 限制总子载波数量 / Clip total subcarriers + if n_sub_s + n_sub_b > self.N: + total = n_sub_s + n_sub_b + n_sub_s = int(round(n_sub_s * self.N / total)) + n_sub_b = self.N - n_sub_s + + # 计算功率分配 / Compute power allocation + p_s = action_s[1] * self.P_max + p_b = action_b[1] * self.P_max + + # 限制总功率 / Limit total power + if p_s + p_b > self.P_max: + total_p = p_s + p_b + p_s = p_s * self.P_max / total_p + p_b = p_b * self.P_max / total_p + + # 解码语义压缩率 / Decode semantic compression ratio + rho = action_s[2] * (self.rho_max - self.rho_min) + self.rho_min + + # 2. 分配子载波 (基于信道质量的贪婪算法) / Allocate subcarriers (greedy by channel quality) + # 计算两组用户的平均信道质量 / Mean channel quality for both groups + sem_channel = np.mean(np.abs(self.channel_gains[self.K_b:])**2, axis=0) if self.K_s > 0 else np.zeros(self.N) + trad_channel = np.mean(np.abs(self.channel_gains[:self.K_b])**2, axis=0) if self.K_b > 0 else np.zeros(self.N) + + # 语义用户优先挑选最好的子载波 / Semantic users pick best subcarriers first + all_subs = np.arange(self.N) + sem_sorted = np.argsort(-sem_channel) + sem_subs = sem_sorted[:n_sub_s] + # 剩余子载波给传统用户 / Remaining subcarriers for traditional users + remaining = np.setdiff1d(all_subs, sem_subs) + + if len(remaining) >= n_sub_b: + trad_quality = trad_channel[remaining] + best_idx = np.argsort(-trad_quality)[:n_sub_b] + trad_subs = remaining[best_idx] + else: + trad_subs = remaining + n_sub_b = len(trad_subs) + + # 3. 功率分配 (组内均分) / Power allocation (equal within group) + noise_power = self.channel_model.noise_power + + # 分配矩阵和功率矩阵 / Allocation and power matrices + alloc_matrix = np.zeros((self.K, self.N)) + power_matrix = np.zeros((self.K, self.N)) + + # 在 K_s 个用户中循环分配语义子载波 / Distribute semantic subcarriers among K_s users round-robin + for i, k in enumerate(range(self.K_b, self.K)): + user_subs = sem_subs[i::max(1, self.K_s)] + if len(user_subs) > 0: + alloc_matrix[k, user_subs] = 1 + power_matrix[k, user_subs] = p_s / max(n_sub_s, 1) + + # 在 K_b 个用户中循环分配传统子载波 / Distribute traditional subcarriers among K_b users round-robin + for i, k in enumerate(range(0, self.K_b)): + user_subs = trad_subs[i::max(1, self.K_b)] + if len(user_subs) > 0: + alloc_matrix[k, user_subs] = 1 + power_matrix[k, user_subs] = p_b / max(n_sub_b, 1) + + # 4. 计算 SNR / Compute SNR - Eq.(8) + snr_matrix = self.channel_model.compute_snr(self.channel_gains, power_matrix, noise_power) + + # 5. 计算每个用户的 QoE / Compute QoE for each user + qoe_list = [] + rates = [] + ssim_values = [] + + # 传统用户 QoE 计算 / Traditional users QoE computation - Eq.(QoE_b) + for k in range(self.K_b): + user_subs = np.where(alloc_matrix[k] > 0)[0] + if len(user_subs) == 0: + rate_k = 0.0 + else: + # R_k = Σ α * Δf * log2(1 + γ) / R_k = Σ α * Δf * log2(1 + γ) + rate_k = np.sum(self.delta_f * np.log2(1 + snr_matrix[k, user_subs])) + rates.append(rate_k) + # QoE_b = min(R_k / R_req, 1) / QoE_b = min(R_k / R_req, 1) + qoe_k = min(rate_k / self.R_req, 1.0) + qoe_list.append(qoe_k) + + # 语义用户 QoE 计算 / Semantic users QoE computation - Eq.(QoE_s) + for k in range(self.K_b, self.K): + user_subs = np.where(alloc_matrix[k] > 0)[0] + if len(user_subs) == 0: + ssim_k = 0.0 + else: + avg_snr = np.mean(snr_matrix[k, user_subs]) + # 计算语义相似度 / Compute SSim - Eq. (SSim) + ssim_k = self.semantic_module.compute_ssim(avg_snr, rho) + ssim_values.append(float(ssim_k)) + # 计算语义 QoE / Compute semantic QoE + qoe_k = self.semantic_module.compute_semantic_qoe(ssim_k, rho, self.w1, self.w2, self.rho_max) + qoe_list.append(qoe_k) + + # 6. 系统平均 QoE / System QoE + qoe_sys = np.mean(qoe_list) if len(qoe_list) > 0 else 0.0 + qoe_s = np.mean(qoe_list[self.K_b:]) if self.K_s > 0 else 0.0 + qoe_b = np.mean(qoe_list[:self.K_b]) if self.K_b > 0 else 0.0 + + # 更新滚动平均值 / Update rolling averages + alpha_smooth = 0.1 + self.qoe_avg_s = alpha_smooth * qoe_s + (1 - alpha_smooth) * self.qoe_avg_s + self.qoe_avg_b = alpha_smooth * qoe_b + (1 - alpha_smooth) * self.qoe_avg_b + # 记录当前分配比例 / Record current allocation ratios + self.alloc_s = n_sub_s / self.N + self.alloc_b = n_sub_b / self.N + + # 7. 为下一步生成新信道 (块衰落) / Regenerate channel for next step (block fading) + self.channel_gains = self.channel_model.generate_channel(self.distances, self.N) + + # 8. 构造输出数据 / Build output + obs_s = self._get_observation('semantic') + obs_b = self._get_observation('traditional') + done = (self.step_count >= self.max_steps) + + # 计算速率满足度 / Compute rate satisfaction for traditional users + if len(rates) > 0: + rate_satisfaction = float(np.mean([1.0 if r >= self.R_req else 0.0 for r in rates])) + else: + rate_satisfaction = 1.0 + + # 构造信息字典 / Construct info dictionary + info = { + 'qoe_semantic': qoe_s, + 'qoe_traditional': qoe_b, + 'qoe_sys': qoe_sys, + 'qoe_list': qoe_list, + 'rates': rates, + 'ssim_values': ssim_values, + 'rate_satisfaction': rate_satisfaction, + 'rho': rho, + 'n_sub_s': n_sub_s, + 'n_sub_b': n_sub_b, + } + + # 返回结果 (奖励值设为各自的平均 QoE) / Return results (rewards set to respective mean QoEs) + return obs_s, obs_b, qoe_s, qoe_b, done, info diff --git a/code/evaluate.py b/code/evaluate.py new file mode 100644 index 0000000..d29968d --- /dev/null +++ b/code/evaluate.py @@ -0,0 +1,577 @@ +#!/usr/bin/env python3 +""" +Co-MADDPG Evaluation & Figure Generation | Co-MADDPG 评估与图表生成 + +This script evaluates trained models across various network scenarios and +generates the 12 primary figures for the research paper. It covers robustness +tests (SNR), scalability (User Load), and internal dynamics (Lambda). + +本脚本在各种网络场景下评估已训练的模型，并为研究论文生成 12 张主要图表。 +它涵盖了鲁棒性测试 (SNR)、可扩展性 (用户负载) 和内部动态 (Lambda)。 + +Scenarios Documented: +1. Convergence / 收敛性 (Fig 2) +2. SNR Sensitivity / SNR 敏感性 (Fig 3, 4) +3. User Load Scalability / 用户负载可扩展性 (Fig 5, 6) +4. Dynamic Lambda Trajectory / 动态 Lambda 轨迹 (Fig 7, 8) +5. Semantic-Traditional Ratio / 语义-传统比例 (Fig 9) +6. Component Ablation / 组件消融实验 (Fig 10) +7. Beta Parameter Sensitivity / Beta 参数敏感性 (Fig 11) +8. Q_th Threshold Sensitivity / Q_th 阈值敏感性 (Fig 12) + +Reference: +- Section VII: Experimental Results +""" + +import os +import sys +import argparse +import json +import yaml +import numpy as np +import torch +from pathlib import Path +from copy import deepcopy + +PROJECT_ROOT = Path(__file__).parent +sys.path.insert(0, str(PROJECT_ROOT)) + +from envs.wireless_env import WirelessEnv +from agents.co_maddpg import CoMADDPG +from baselines.pure_coop import PureCooperative +from baselines.pure_comp import PureCompetitive +from baselines.single_dqn import SingleAgentDQN +from baselines.iddpg import IndependentDDPG +from baselines.fixed_lambda import FixedLambda +from baselines.equal_alloc import EqualAllocation +from baselines.semantic_only import SemanticOnly +from utils.metrics import jain_fairness, rate_satisfaction, compute_system_qoe, moving_average +from utils.visualization import Plotter + + +# Mapping internal keys to display names and classes +# 将内部键映射到显示名称和类 +ALGO_MAP = { + 'co_maddpg': ('Co-MADDPG', CoMADDPG), + 'pure_coop': ('Pure Cooperative', PureCooperative), + 'pure_comp': ('Pure Competitive', PureCompetitive), + 'single_dqn': ('Single-Agent DQN', SingleAgentDQN), + 'iddpg': ('IDDPG', IndependentDDPG), + 'fixed_lambda': ('Fixed λ=0.5', FixedLambda), + 'equal_alloc': ('Equal Allocation', EqualAllocation), + 'semantic_only': ('Semantic-Only', SemanticOnly), +} + + +def load_config(config_path: str) -> dict: + """Load YAML configuration file. | 加载 YAML 配置文件。""" + with open(config_path, 'r', encoding='utf-8') as f: + return yaml.safe_load(f) + + +def evaluate_episode(env, agent, config, num_episodes=10): + """ + Run evaluation episodes and return average metrics. + 执行评估回合并返回平均指标。 + + Parameters + ---------- + env : WirelessEnv + The wireless environment instance. | 无线环境实例。 + agent : BaseAgent + The trained agent model. | 已训练的智能体模型。 + config : dict + Configuration parameters. | 配置参数。 + num_episodes : int + Number of episodes to average over. | 用于计算平均值的回合数。 + """ + max_steps = config['training']['max_steps'] + + all_qoe_sys = [] + all_qoe_s = [] + all_qoe_b = [] + all_fairness = [] + all_rate_sat = [] + all_lambda = [] + all_rates = [] + + for _ in range(num_episodes): + obs_s, obs_b = env.reset() + ep_qoe_sys = [] + ep_lambda = [] + + for step in range(max_steps): + # Deterministic action selection (no exploration noise) + # 确定性动作选择（无探索噪声） + act_s, act_b = agent.select_action(obs_s, obs_b, explore=False) + next_obs_s, next_obs_b, qoe_s, qoe_b, done, info = env.step(act_s, act_b) + + qoe_sys = info['qoe_sys'] + # Get lambda if applicable | 获取 lambda（如果适用） + if hasattr(agent, 'compute_lambda'): + lambda_val = agent.compute_lambda(qoe_sys) + else: + lambda_val = 0.5 + + ep_qoe_sys.append(qoe_sys) + ep_lambda.append(lambda_val) + + obs_s = next_obs_s + obs_b = next_obs_b + if done: + break + + # Calculate episode means | 计算回合平均值 + all_qoe_sys.append(np.mean(ep_qoe_sys)) + all_qoe_s.append(info['qoe_semantic']) + all_qoe_b.append(info['qoe_traditional']) + all_fairness.append(jain_fairness(info['qoe_list'])) + all_rate_sat.append(info['rate_satisfaction']) + all_lambda.append(np.mean(ep_lambda)) + all_rates.extend(info['rates']) + + return { + 'qoe_sys': np.mean(all_qoe_sys), + 'qoe_sys_std': np.std(all_qoe_sys), + 'qoe_semantic': np.mean(all_qoe_s), + 'qoe_traditional': np.mean(all_qoe_b), + 'fairness': np.mean(all_fairness), + 'rate_satisfaction': np.mean(all_rate_sat), + 'avg_lambda': np.mean(all_lambda), + 'lambda_trajectory': all_lambda, + } + + +# ============================================================ +# Scenario 1: Convergence (Fig 2) +# ============================================================ +def scenario_convergence(results_dir: str, save_dir: str): + """ + Generate convergence curves from training history. + 根据训练历史生成收敛曲线。 + + Loads JSON history files for each algorithm and plots system QoE. + 加载每个算法的 JSON 历史文件并绘制系统 QoE。 + """ + print("\n[Scenario 1] Convergence curves (Fig 2)") + plotter = Plotter() + data_dict = {} + + for algo_key, (display_name, _) in ALGO_MAP.items(): + history_path = os.path.join(results_dir, f'{algo_key}_history.json') + if os.path.exists(history_path): + with open(history_path, 'r') as f: + history = json.load(f) + if 'episode_qoe_sys' in history: + data_dict[display_name] = history['episode_qoe_sys'] + + if data_dict: + plotter.plot_convergence(data_dict, os.path.join(save_dir, 'fig2_convergence')) + print(f" Saved fig2_convergence") + else: + print(" No training history found. Run training first.") + + +# ============================================================ +# Scenario 2: QoE vs SNR (Fig 3, 4) +# ============================================================ +def scenario_snr(config: dict, results_dir: str, save_dir: str, num_eval=5): + """ + Evaluate performance across different SNR levels. + 在不同 SNR 水平下评估性能。 + + Simulation Method: Adjusts noise PSD to achieve target SNR (0 to 30 dB). + 仿真方法：调整噪声功率谱密度 (PSD) 以达到目标 SNR（0 到 30 dB）。 + """ + print("\n[Scenario 2] QoE vs SNR (Fig 3, 4)") + plotter = Plotter() + snr_levels_db = np.arange(0, 31, 5) # 0, 5, 10, 15, 20, 25, 30 + + qoe_data = {} + fairness_data = {} + + for algo_key, (display_name, AlgoClass) in ALGO_MAP.items(): + qoe_vals = [] + fair_vals = [] + + for snr_db in snr_levels_db: + # Modify noise PSD to achieve target SNR | 修改噪声 PSD 以达到目标 SNR + test_config = deepcopy(config) + # SNR = Signal_Power - Noise_Power. Adjusting noise_psd shifts SNR. + # SNR = 信号功率 - 噪声功率。调整 noise_psd 会改变 SNR。 + snr_offset = snr_db - 15 # 15 dB is roughly the baseline SNR | 15 dB 大约是基准 SNR + test_config['env']['noise_psd'] = -174 - snr_offset + + env = WirelessEnv(test_config) + agent = AlgoClass(test_config) + + # Load trained model weights | 加载已训练的模型权重 + model_path = os.path.join(results_dir, f'{algo_key}_best.pt') + if os.path.exists(model_path) and hasattr(agent, 'load'): + try: + agent.load(model_path) + except Exception: + pass + + result = evaluate_episode(env, agent, test_config, num_episodes=num_eval) + qoe_vals.append(result['qoe_sys']) + fair_vals.append(result['fairness']) + + qoe_data[display_name] = qoe_vals + fairness_data[display_name] = fair_vals + print(f" {display_name}: QoE range [{min(qoe_vals):.3f}, {max(qoe_vals):.3f}]") + + plotter.plot_qoe_vs_snr(qoe_data, os.path.join(save_dir, 'fig3_qoe_vs_snr')) + plotter.plot_fairness_vs_snr(fairness_data, os.path.join(save_dir, 'fig4_fairness_vs_snr')) + print(f" Saved fig3, fig4") + + return {'snr_levels': snr_levels_db.tolist(), 'qoe': qoe_data, 'fairness': fairness_data} + + +# ============================================================ +# Scenario 3: QoE vs User Load (Fig 5, 6) +# ============================================================ +def scenario_user_load(config: dict, results_dir: str, save_dir: str, num_eval=5): + """ + Evaluate performance with different user counts. + 评估不同用户数量下的性能。 + + Simulation Method: Varies total user count K from 4 to 12, split between S and B. + 仿真方法：将总用户数 K 在 4 到 12 之间变化，在语义 (S) 和传统 (B) 用户之间分配。 + """ + print("\n[Scenario 3] QoE vs User Load (Fig 5, 6)") + plotter = Plotter() + user_counts = [4, 6, 8, 10, 12] # Total K | 总用户数 K + + qoe_data = {} + rate_sat_data = {} + + for algo_key, (display_name, AlgoClass) in ALGO_MAP.items(): + qoe_vals = [] + rate_vals = [] + + for k_total in user_counts: + test_config = deepcopy(config) + # Distribute users equally between types | 在不同类型之间平均分配用户 + k_s = k_total // 2 + k_b = k_total - k_s + test_config['env']['num_semantic_users'] = k_s + test_config['env']['num_traditional_users'] = k_b + + env = WirelessEnv(test_config) + agent = AlgoClass(test_config) + + model_path = os.path.join(results_dir, f'{algo_key}_best.pt') + if os.path.exists(model_path) and hasattr(agent, 'load'): + try: + agent.load(model_path) + except Exception: + pass + + result = evaluate_episode(env, agent, test_config, num_episodes=num_eval) + qoe_vals.append(result['qoe_sys']) + rate_vals.append(result['rate_satisfaction']) + + qoe_data[display_name] = qoe_vals + rate_sat_data[display_name] = rate_vals + print(f" {display_name}: QoE range [{min(qoe_vals):.3f}, {max(qoe_vals):.3f}]") + + plotter.plot_qoe_vs_users(qoe_data, os.path.join(save_dir, 'fig5_qoe_vs_users')) + plotter.plot_rate_satisfaction_vs_users(rate_sat_data, os.path.join(save_dir, 'fig6_rate_sat_vs_users')) + print(f" Saved fig5, fig6") + + +# ============================================================ +# Scenario 4: Lambda Dynamics (Fig 7, 8) +# ============================================================ +def scenario_lambda_dynamics(config: dict, results_dir: str, save_dir: str): + """ + Analyze dynamic λ switching behavior of Co-MADDPG. + 分析 Co-MADDPG 的动态 λ 切换行为。 + """ + print("\n[Scenario 4] Lambda Dynamics (Fig 7, 8)") + plotter = Plotter() + + env = WirelessEnv(config) + agent = CoMADDPG(config) + + model_path = os.path.join(results_dir, 'co_maddpg_best.pt') + if os.path.exists(model_path): + try: + agent.load(model_path) + except Exception: + pass + + # Run one episode and collect λ trajectory | 执行一个回合并收集 λ 轨迹 + obs_s, obs_b = env.reset() + lambda_vals = [] + qoe_vals = [] + + for step in range(config['training']['max_steps']): + act_s, act_b = agent.select_action(obs_s, obs_b, explore=False) + next_obs_s, next_obs_b, qoe_s, qoe_b, done, info = env.step(act_s, act_b) + + qoe_sys = info['qoe_sys'] + lambda_val = agent.compute_lambda(qoe_sys) + lambda_vals.append(float(lambda_val)) + qoe_vals.append(float(qoe_sys)) + + obs_s, obs_b = next_obs_s, next_obs_b + if done: + break + + plotter.plot_lambda_trajectory(lambda_vals, os.path.join(save_dir, 'fig7_lambda_trajectory')) + plotter.plot_lambda_qoe_scatter(lambda_vals, qoe_vals, os.path.join(save_dir, 'fig8_lambda_qoe_scatter')) + print(f" Saved fig7, fig8") + + +# ============================================================ +# Scenario 5: Semantic/Traditional Ratio (Fig 9) +# ============================================================ +def scenario_user_ratio(config: dict, results_dir: str, save_dir: str, num_eval=5): + """ + Evaluate with different semantic/traditional user ratios. + 评估不同语义/传统用户比例下的性能。 + + Studies the impact as semantic communication becomes more prevalent. + 研究语义通信变得更加普遍时的影响。 + """ + print("\n[Scenario 5] User Ratio Analysis (Fig 9)") + plotter = Plotter() + + total_users = 6 + ratios = [0.0, 0.17, 0.33, 0.5, 0.67, 0.83, 1.0] # semantic fraction | 语义用户占比 + + qoe_data = {} + + for algo_key, (display_name, AlgoClass) in ALGO_MAP.items(): + qoe_vals = [] + + for ratio in ratios: + # Map ratio to discrete integer counts | 将比例映射为离散整数计数 + k_s = max(0, min(total_users, int(round(ratio * total_users)))) + k_b = total_users - k_s + # Ensure at least one of each for hybrid env constraints if necessary + # 如有必要，确保混合环境约束下每种类型至少有一个 + if k_s == 0: k_s = 1; k_b = total_users - 1 + if k_b == 0: k_b = 1; k_s = total_users - 1 + + test_config = deepcopy(config) + test_config['env']['num_semantic_users'] = k_s + test_config['env']['num_traditional_users'] = k_b + + env = WirelessEnv(test_config) + agent = AlgoClass(test_config) + + model_path = os.path.join(results_dir, f'{algo_key}_best.pt') + if os.path.exists(model_path) and hasattr(agent, 'load'): + try: + agent.load(model_path) + except Exception: + pass + + result = evaluate_episode(env, agent, test_config, num_episodes=num_eval) + qoe_vals.append(result['qoe_sys']) + + qoe_data[display_name] = qoe_vals + + plotter.plot_qoe_vs_ratio(qoe_data, ratios, os.path.join(save_dir, 'fig9_qoe_vs_ratio')) + print(f" Saved fig9") + + +# ============================================================ +# Scenario 6: Ablation Study (Fig 10) +# ============================================================ +def scenario_ablation(config: dict, results_dir: str, save_dir: str, num_eval=5): + """ + Run ablation study comparing core components. + 运行消融实验比较核心组件。 + + Ablation Mapping: + - w/o Stackelberg: Pure Cooperative (simultaneous update) | 无 Stackelberg：纯协作（同步更新） + - w/o Dynamic λ: Fixed Lambda (λ=0.5) | 无动态 λ：固定 Lambda (λ=0.5) + - w/o Cooperation: Pure Competitive (λ=0) | 无协作：纯竞争 (λ=0) + - w/o CTDE: IDDPG (Independent Critics) | 无 CTDE：IDDPG（独立评论家） + """ + print("\n[Scenario 6] Ablation Study (Fig 10)") + plotter = Plotter() + + ablation_keys = { + 'Co-MADDPG (Full)': 'co_maddpg', + 'w/o Stackelberg': 'pure_coop', + 'w/o Dynamic λ': 'fixed_lambda', + 'w/o Cooperation': 'pure_comp', + 'w/o CTDE': 'iddpg', + } + + ablation_data = {} + for label, algo_key in ablation_keys.items(): + history_path = os.path.join(results_dir, f'{algo_key}_history.json') + if os.path.exists(history_path): + with open(history_path, 'r') as f: + history = json.load(f) + # Average of last 500 episodes for stability | 为保证稳定性取最后 500 回合的平均值 + qoe_series = history.get('episode_qoe_sys', []) + if len(qoe_series) >= 500: + ablation_data[label] = np.mean(qoe_series[-500:]) + elif len(qoe_series) > 0: + ablation_data[label] = np.mean(qoe_series[-len(qoe_series)//5:]) + else: + ablation_data[label] = 0.0 + else: + # Fallback to direct evaluation if history missing | 如果历史记录缺失，则回退到直接评估 + env = WirelessEnv(config) + AlgoClass = ALGO_MAP[algo_key][1] + agent = AlgoClass(config) + model_path = os.path.join(results_dir, f'{algo_key}_best.pt') + if os.path.exists(model_path) and hasattr(agent, 'load'): + try: + agent.load(model_path) + except Exception: + pass + result = evaluate_episode(env, agent, config, num_episodes=num_eval) + ablation_data[label] = result['qoe_sys'] + + plotter.plot_ablation(ablation_data, os.path.join(save_dir, 'fig10_ablation')) + print(f" Saved fig10") + + +# ============================================================ +# Scenario 7: β Sensitivity (Fig 11) +# ============================================================ +def scenario_beta_sensitivity(config: dict, results_dir: str, save_dir: str, num_eval=5): + """ + Evaluate sensitivity to the β parameter in the sigmoid function. + 评估 Sigmoid 函数中 β 参数的敏感性。 + + β controls the steepness of switching between competition and cooperation. + β 控制竞争与协作之间切换的陡峭程度。 + """ + print("\n[Scenario 7] β Sensitivity (Fig 11)") + plotter = Plotter() + + betas = [1, 3, 5, 7, 10] + qoe_data = {} + + for beta in betas: + test_config = deepcopy(config) + test_config['training']['beta'] = float(beta) + + env = WirelessEnv(test_config) + agent = CoMADDPG(test_config) + + model_path = os.path.join(results_dir, 'co_maddpg_best.pt') + if os.path.exists(model_path): + try: + agent.load(model_path) + except Exception: + pass + + result = evaluate_episode(env, agent, test_config, num_episodes=num_eval) + qoe_data[f'β={beta}'] = result['qoe_sys'] + print(f" β={beta}: QoE_sys={result['qoe_sys']:.4f}") + + plotter.plot_beta_sensitivity(qoe_data, betas, os.path.join(save_dir, 'fig11_beta_sensitivity')) + print(f" Saved fig11") + + +# ============================================================ +# Scenario 8: Q_th Sensitivity (Fig 12) +# ============================================================ +def scenario_qth_sensitivity(config: dict, results_dir: str, save_dir: str, num_eval=5): + """ + Evaluate sensitivity to the Q_th threshold parameter. + 评估 Q_th 阈值参数的敏感性。 + + Q_th is the target QoE level below which cooperation is triggered. + Q_th 是触发协作的目标 QoE 水平。 + """ + print("\n[Scenario 8] Q_th Sensitivity (Fig 12)") + plotter = Plotter() + + qths = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8] + qoe_data = {} + + for qth in qths: + test_config = deepcopy(config) + test_config['training']['q_threshold'] = float(qth) + + env = WirelessEnv(test_config) + agent = CoMADDPG(test_config) + + model_path = os.path.join(results_dir, 'co_maddpg_best.pt') + if os.path.exists(model_path): + try: + agent.load(model_path) + except Exception: + pass + + result = evaluate_episode(env, agent, test_config, num_episodes=num_eval) + qoe_data[f'Q_th={qth}'] = result['qoe_sys'] + print(f" Q_th={qth}: QoE_sys={result['qoe_sys']:.4f}") + + plotter.plot_qth_sensitivity(qoe_data, qths, os.path.join(save_dir, 'fig12_qth_sensitivity')) + print(f" Saved fig12") + + +# ============================================================ +# Run all scenarios | 执行所有场景 +# ============================================================ +def run_all_scenarios(config: dict, results_dir: str, save_dir: str): + """Run all evaluation scenarios and generate all figures. | 执行所有评估场景并生成所有图表。""" + os.makedirs(save_dir, exist_ok=True) + + scenario_convergence(results_dir, save_dir) + scenario_snr(config, results_dir, save_dir) + scenario_user_load(config, results_dir, save_dir) + scenario_lambda_dynamics(config, results_dir, save_dir) + scenario_user_ratio(config, results_dir, save_dir) + scenario_ablation(config, results_dir, save_dir) + scenario_beta_sensitivity(config, results_dir, save_dir) + scenario_qth_sensitivity(config, results_dir, save_dir) + + print(f"\nAll figures saved to: {save_dir}") + + +def main(): + """Main entry point for evaluation. | 评估主入口。""" + parser = argparse.ArgumentParser(description='Co-MADDPG Evaluation') + parser.add_argument('--config', type=str, default='configs/default.yaml', + help='Path to config YAML') + parser.add_argument('--results_dir', type=str, required=True, + help='Directory with trained models and history') + parser.add_argument('--save_dir', type=str, default=None, + help='Directory to save figures (default: results_dir/figures)') + parser.add_argument('--scenario', type=str, default='all', + choices=['all', 'convergence', 'snr', 'user_load', + 'lambda', 'ratio', 'ablation', 'beta', 'qth'], + help='Evaluation scenario to run') + parser.add_argument('--num_eval', type=int, default=10, + help='Number of evaluation episodes per setting') + args = parser.parse_args() + + config_path = os.path.join(PROJECT_ROOT, args.config) + config = load_config(config_path) + + save_dir = args.save_dir or os.path.join(args.results_dir, 'figures') + os.makedirs(save_dir, exist_ok=True) + + # Dispatch to specific scenario | 分派到特定场景 + scenario_map = { + 'all': lambda: run_all_scenarios(config, args.results_dir, save_dir), + 'convergence': lambda: scenario_convergence(args.results_dir, save_dir), + 'snr': lambda: scenario_snr(config, args.results_dir, save_dir, args.num_eval), + 'user_load': lambda: scenario_user_load(config, args.results_dir, save_dir, args.num_eval), + 'lambda': lambda: scenario_lambda_dynamics(config, args.results_dir, save_dir), + 'ratio': lambda: scenario_user_ratio(config, args.results_dir, save_dir, args.num_eval), + 'ablation': lambda: scenario_ablation(config, args.results_dir, save_dir, args.num_eval), + 'beta': lambda: scenario_beta_sensitivity(config, args.results_dir, save_dir, args.num_eval), + 'qth': lambda: scenario_qth_sensitivity(config, args.results_dir, save_dir, args.num_eval), + } + + scenario_map[args.scenario]() + print("\nEvaluation complete!") + + +if __name__ == '__main__': + main() diff --git a/code/results/run_20260228_153632/config.yaml b/code/results/run_20260228_153632/config.yaml new file mode 100644 index 0000000..3008ca3 --- /dev/null +++ b/code/results/run_20260228_153632/config.yaml @@ -0,0 +1,47 @@ +env: + bandwidth: 10.0e6 + carrier_freq: 3.5 + max_distance: 500 + max_power: 1.0 + min_distance: 50 + min_rate_req: 500.0e3 + noise_psd: -174 + num_semantic_users: 3 + num_subcarriers: 64 + num_traditional_users: 3 + rho_max: 1.0 + rho_min: 0.05 + subcarrier_spacing: 156250.0 + w1: 0.7 + w2: 0.3 +network: + actor_hidden: + - 256 + - 256 + - 128 + critic_hidden: + - 512 + - 512 + - 256 +reward: + comp_self: 0.8 + comp_sys: 0.2 + coop_other: 0.3 + coop_self: 0.5 + coop_sys: 0.2 +training: + actor_lr: 0.0001 + batch_size: 256 + beta: 5.0 + buffer_capacity: 100000 + critic_lr: 0.0003 + gamma: 0.95 + max_episodes: 3 + max_steps: 10 + ou_sigma_init: 0.2 + ou_sigma_min: 0.01 + ou_theta: 0.15 + q_threshold: 0.6 + seed: 42 + tau: 0.01 + update_interval: 5 diff --git a/code/results/run_20260228_153837/co_maddpg_best.pt b/code/results/run_20260228_153837/co_maddpg_best.pt new file mode 100644 index 0000000..b1dabcb --- /dev/null +++ b/code/results/run_20260228_153837/co_maddpg_best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d0702b7ea25fc156390253522c1c3137ea293b2cb807eafdfbd9bb74eb27d9d +size 4685645 diff --git a/code/results/run_20260228_153837/co_maddpg_final.pt b/code/results/run_20260228_153837/co_maddpg_final.pt new file mode 100644 index 0000000..67824cb --- /dev/null +++ b/code/results/run_20260228_153837/co_maddpg_final.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:46a727f4c598b2bccfc7d5dfe4f29f050c48513c376f9d34d4b988c531fd08d5 +size 4685683 diff --git a/code/results/run_20260228_153837/co_maddpg_history.json b/code/results/run_20260228_153837/co_maddpg_history.json new file mode 100644 index 0000000..f259b8a --- /dev/null +++ b/code/results/run_20260228_153837/co_maddpg_history.json @@ -0,0 +1,43 @@ +{ + "episode_qoe_sys": [ + 0.7113027844231694, + 0.6344297213112167, + 0.7739924098489253 + ], + "episode_qoe_semantic": [ + 0.4226055688463388, + 0.2688594426224332, + 0.5479848196978504 + ], + "episode_qoe_traditional": [ + 1.0, + 1.0, + 1.0 + ], + "episode_lambda": [ + 0.6207547547571596, + 0.5395092957443972, + 0.6895618004798005 + ], + "episode_fairness": [ + 0.7661071139341958, + 0.7078592991985423, + 0.8700568715234 + ], + "episode_rate_satisfaction": [ + 1.0, + 1.0, + 1.0 + ], + "episode_reward_s": [ + 5.776441792194522, + 4.562596418470474, + 6.7501691441542295 + ], + "episode_reward_b": [ + 8.449613896268866, + 8.125998007753859, + 8.729679052824276 + ], + "training_time": 0.04235124588012695 +} \ No newline at end of file diff --git a/code/results/run_20260228_153837/config.yaml b/code/results/run_20260228_153837/config.yaml new file mode 100644 index 0000000..3c1abc5 --- /dev/null +++ b/code/results/run_20260228_153837/config.yaml @@ -0,0 +1,47 @@ +env: + bandwidth: 10000000.0 + carrier_freq: 3.5 + max_distance: 500 + max_power: 1.0 + min_distance: 50 + min_rate_req: 500000.0 + noise_psd: -174 + num_semantic_users: 3 + num_subcarriers: 64 + num_traditional_users: 3 + rho_max: 1.0 + rho_min: 0.05 + subcarrier_spacing: 156250.0 + w1: 0.7 + w2: 0.3 +network: + actor_hidden: + - 256 + - 256 + - 128 + critic_hidden: + - 512 + - 512 + - 256 +reward: + comp_self: 0.8 + comp_sys: 0.2 + coop_other: 0.3 + coop_self: 0.5 + coop_sys: 0.2 +training: + actor_lr: 0.0001 + batch_size: 256 + beta: 5.0 + buffer_capacity: 100000 + critic_lr: 0.0003 + gamma: 0.95 + max_episodes: 3 + max_steps: 10 + ou_sigma_init: 0.2 + ou_sigma_min: 0.01 + ou_theta: 0.15 + q_threshold: 0.6 + seed: 42 + tau: 0.01 + update_interval: 5 diff --git a/code/results/run_20260228_153858/config.yaml b/code/results/run_20260228_153858/config.yaml new file mode 100644 index 0000000..3c1abc5 --- /dev/null +++ b/code/results/run_20260228_153858/config.yaml @@ -0,0 +1,47 @@ +env: + bandwidth: 10000000.0 + carrier_freq: 3.5 + max_distance: 500 + max_power: 1.0 + min_distance: 50 + min_rate_req: 500000.0 + noise_psd: -174 + num_semantic_users: 3 + num_subcarriers: 64 + num_traditional_users: 3 + rho_max: 1.0 + rho_min: 0.05 + subcarrier_spacing: 156250.0 + w1: 0.7 + w2: 0.3 +network: + actor_hidden: + - 256 + - 256 + - 128 + critic_hidden: + - 512 + - 512 + - 256 +reward: + comp_self: 0.8 + comp_sys: 0.2 + coop_other: 0.3 + coop_self: 0.5 + coop_sys: 0.2 +training: + actor_lr: 0.0001 + batch_size: 256 + beta: 5.0 + buffer_capacity: 100000 + critic_lr: 0.0003 + gamma: 0.95 + max_episodes: 3 + max_steps: 10 + ou_sigma_init: 0.2 + ou_sigma_min: 0.01 + ou_theta: 0.15 + q_threshold: 0.6 + seed: 42 + tau: 0.01 + update_interval: 5 diff --git a/code/results/run_20260228_153858/pure_coop_best.pt/actor_b.pth b/code/results/run_20260228_153858/pure_coop_best.pt/actor_b.pth new file mode 100644 index 0000000..d8f1b5e --- /dev/null +++ b/code/results/run_20260228_153858/pure_coop_best.pt/actor_b.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ccdec3baf1507436f06aa3581ce9d30f5299e12a25e05c1104def6a02c726c0d +size 470157 diff --git a/code/results/run_20260228_153858/pure_coop_best.pt/actor_s.pth b/code/results/run_20260228_153858/pure_coop_best.pt/actor_s.pth new file mode 100644 index 0000000..196ee6b --- /dev/null +++ b/code/results/run_20260228_153858/pure_coop_best.pt/actor_s.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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+#!/usr/bin/env python3 +""" +Co-MADDPG Training Entry Point | Co-MADDPG 训练入口脚本 + +This script manages the training process for Co-MADDPG and various baseline +algorithms in a semantic-traditional hybrid wireless resource allocation +environment. It handles configuration loading, environment initialization, +the training loop, and result logging. + +本脚本管理在语义-传统混合无线资源分配环境下的 Co-MADDPG 及各基准算法的训练过程。 +它负责加载配置、初始化环境、训练循环以及结果记录。 + +Core Components: +- CLI Argument Parsing / 命令行参数解析 +- Training Loop with Dynamic Rewards / 带有动态奖励的训练循环 +- Model Saving & Checkpointing / 模型保存与断点保存 +- Performance Metric Tracking / 性能指标追踪 + +Reference: +- "Dynamic Cooperative-Competitive Multi-Agent Reinforcement Learning for + Resource Allocation in Semantic-Traditional Hybrid Wireless Networks" +""" + +import os +import sys +import argparse +import time +import json +import yaml +import numpy as np +import torch +from pathlib import Path +from datetime import datetime + +# Add project root to path | 将项目根目录添加到路径中 +PROJECT_ROOT = Path(__file__).parent +sys.path.insert(0, str(PROJECT_ROOT)) + +from envs.wireless_env import WirelessEnv +from agents.co_maddpg import CoMADDPG +from baselines.pure_coop import PureCooperative +from baselines.pure_comp import PureCompetitive +from baselines.single_dqn import SingleAgentDQN +from baselines.iddpg import IndependentDDPG +from baselines.fixed_lambda import FixedLambda +from baselines.equal_alloc import EqualAllocation +from baselines.semantic_only import SemanticOnly +from utils.metrics import compute_system_qoe, jain_fairness, rate_satisfaction, moving_average + + +def load_config(config_path: str) -> dict: + """ + Load YAML configuration file. | 加载 YAML 配置文件。 + """ + with open(config_path, 'r', encoding='utf-8') as f: + config = yaml.safe_load(f) + return config + + +def get_algorithm(name: str, config: dict): + """ + Instantiate algorithm by name. | 按名称实例化算法。 + + The agent interface typically follows this contract: + 1. select_action(obs_s, obs_b, explore): Choose actions for semantic/traditional agents. + 2. compute_rewards(qoe_s, qoe_b, qoe_sys): Calculate mixed rewards based on lambda. + 3. update(): Perform one optimization step using replay buffer data. + 4. buffer / replay_buffer: Store transitions (s, a, r, s', done). + + 智能体接口通常遵循以下契约： + 1. select_action(obs_s, obs_b, explore)：为语义/传统智能体选择动作。 + 2. compute_rewards(qoe_s, qoe_b, qoe_sys)：基于 lambda 计算混合奖励。 + 3. update()：使用经验回放池数据执行一个优化步骤。 + 4. buffer / replay_buffer：存储转换元组 (s, a, r, s', done)。 + """ + algorithms = { + 'co_maddpg': CoMADDPG, + 'pure_coop': PureCooperative, + 'pure_comp': PureCompetitive, + 'single_dqn': SingleAgentDQN, + 'iddpg': IndependentDDPG, + 'fixed_lambda': FixedLambda, + 'equal_alloc': EqualAllocation, + 'semantic_only': SemanticOnly, + } + if name not in algorithms: + raise ValueError(f"Unknown algorithm: {name}. Choose from {list(algorithms.keys())}") + return algorithms[name](config) + + +ALGO_DISPLAY_NAMES = { + 'co_maddpg': 'Co-MADDPG', + 'pure_coop': 'Pure Cooperative', + 'pure_comp': 'Pure Competitive', + 'single_dqn': 'Single-Agent DQN', + 'iddpg': 'IDDPG', + 'fixed_lambda': 'Fixed λ=0.5', + 'equal_alloc': 'Equal Allocation', + 'semantic_only': 'Semantic-Only', +} + + +def train_single(algorithm_name: str, config: dict, save_dir: str) -> dict: + """ + Train a single algorithm and return training history. | 训练单个算法并返回训练历史。 + + Workflow / 工作流程: + 1. Initialize Environment & Agent / 初始化环境和智能体 + 2. Outer Loop: Episodes / 外层循环：回合 + 3. Inner Loop: Steps / 内层循环：步数 + 4. Reward computation & Replay Buffer storage / 奖励计算与经验回放存储 + 5. Policy update & Noise decay / 策略更新与噪声衰减 + + Returns: + dict with training metrics over episodes. | 包含各回合训练指标的字典。 + """ + print(f"\n{'='*60}") + print(f"Training: {ALGO_DISPLAY_NAMES.get(algorithm_name, algorithm_name)}") + print(f"{'='*60}") + + # Set random seeds | 设置随机种子 + seed = config['training'].get('seed', 42) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + + # Create environment and agent | 创建环境和智能体 + env = WirelessEnv(config) + agent = get_algorithm(algorithm_name, config) + + max_episodes = config['training']['max_episodes'] + max_steps = config['training']['max_steps'] + update_interval = config['training'].get('update_interval', 5) + + # Training history initialization | 训练历史记录初始化 + history = { + 'episode_qoe_sys': [], + 'episode_qoe_semantic': [], + 'episode_qoe_traditional': [], + 'episode_lambda': [], + 'episode_fairness': [], + 'episode_rate_satisfaction': [], + 'episode_reward_s': [], + 'episode_reward_b': [], + } + + start_time = time.time() + best_qoe = -float('inf') + + # Episode loop | 回合循环 + for episode in range(1, max_episodes + 1): + obs_s, obs_b = env.reset() + + ep_qoe_sys_list = [] + ep_qoe_s_list = [] + ep_qoe_b_list = [] + ep_lambda_list = [] + ep_fairness_list = [] + ep_rate_sat_list = [] + ep_reward_s_total = 0.0 + ep_reward_b_total = 0.0 + + # Noise decay mechanism | 噪声衰减机制 + # Reduces exploration over time to stabilize policy | 随着时间推移减少探索以稳定策略 + if hasattr(agent, 'noise_s'): + agent.noise_s.decay_sigma(episode) + if hasattr(agent, 'noise_b'): + agent.noise_b.decay_sigma(episode) + + # Step loop | 步数循环 + for step in range(1, max_steps + 1): + # 1. Action Selection | 动作选择 + act_s, act_b = agent.select_action(obs_s, obs_b, explore=True) + + # 2. Environment Interaction | 环境交互 + next_obs_s, next_obs_b, qoe_s, qoe_b, done, info = env.step(act_s, act_b) + + qoe_sys = info['qoe_sys'] + + # 3. Reward Calculation | 奖励计算 + # Uses dynamic lambda for Co-MADDPG | Co-MADDPG 使用动态 lambda + if hasattr(agent, 'compute_rewards'): + r_s, r_b, lambda_val = agent.compute_rewards(qoe_s, qoe_b, qoe_sys) + else: + r_s, r_b, lambda_val = qoe_s, qoe_b, 0.5 + + # 4. Storage in Replay Buffer | 存储在经验回放池 + if hasattr(agent, 'buffer'): + agent.buffer.push(obs_s, obs_b, act_s, act_b, r_s, r_b, next_obs_s, next_obs_b, done) + elif hasattr(agent, 'replay_buffer'): + agent.replay_buffer.push(obs_s, obs_b, act_s, act_b, r_s, r_b, next_obs_s, next_obs_b, done) + + # 5. Agent Update | 智能体更新 + # Updates occur every few steps to improve training efficiency | 每隔几步更新一次以提高训练效率 + if step % update_interval == 0: + agent.update() + + # Metric tracking | 指标追踪 + ep_qoe_sys_list.append(qoe_sys) + ep_qoe_s_list.append(qoe_s) + ep_qoe_b_list.append(qoe_b) + ep_lambda_list.append(lambda_val) + ep_fairness_list.append(jain_fairness(info['qoe_list'])) + ep_rate_sat_list.append(info['rate_satisfaction']) + ep_reward_s_total += r_s + ep_reward_b_total += r_b + + obs_s = next_obs_s + obs_b = next_obs_b + + if done: + break + + # Record episode average metrics | 记录回合平均指标 + avg_qoe_sys = np.mean(ep_qoe_sys_list) + avg_qoe_s = np.mean(ep_qoe_s_list) + avg_qoe_b = np.mean(ep_qoe_b_list) + avg_lambda = np.mean(ep_lambda_list) + avg_fairness = np.mean(ep_fairness_list) + avg_rate_sat = np.mean(ep_rate_sat_list) + + history['episode_qoe_sys'].append(avg_qoe_sys) + history['episode_qoe_semantic'].append(avg_qoe_s) + history['episode_qoe_traditional'].append(avg_qoe_b) + history['episode_lambda'].append(avg_lambda) + history['episode_fairness'].append(avg_fairness) + history['episode_rate_satisfaction'].append(avg_rate_sat) + history['episode_reward_s'].append(ep_reward_s_total) + history['episode_reward_b'].append(ep_reward_b_total) + + # Best model checkpointing | 最佳模型断点保存 + if avg_qoe_sys > best_qoe: + best_qoe = avg_qoe_sys + model_path = os.path.join(save_dir, f'{algorithm_name}_best.pt') + if hasattr(agent, 'save'): + agent.save(model_path) + + # Logging / 日志记录 + if episode % 100 == 0 or episode == 1: + elapsed = time.time() - start_time + print(f" Ep {episode:5d}/{max_episodes} | " + f"QoE_sys: {avg_qoe_sys:.4f} | " + f"QoE_s: {avg_qoe_s:.4f} | " + f"QoE_b: {avg_qoe_b:.4f} | " + f"λ: {avg_lambda:.3f} | " + f"Fair: {avg_fairness:.3f} | " + f"RateSat: {avg_rate_sat:.2f} | " + f"Time: {elapsed:.0f}s") + + total_time = time.time() - start_time + history['training_time'] = total_time + + # Final model save | 最终模型保存 + final_model_path = os.path.join(save_dir, f'{algorithm_name}_final.pt') + if hasattr(agent, 'save'): + agent.save(final_model_path) + + # Save training history as JSON | 以 JSON 格式保存训练历史 + history_path = os.path.join(save_dir, f'{algorithm_name}_history.json') + serializable_history = {k: [float(v) for v in vals] if isinstance(vals, list) else float(vals) + for k, vals in history.items()} + with open(history_path, 'w') as f: + json.dump(serializable_history, f, indent=2) + + print(f"\n Training complete! Time: {total_time:.1f}s | Best QoE_sys: {best_qoe:.4f}") + print(f" Model saved to: {final_model_path}") + print(f" History saved to: {history_path}") + + return history + + +def train_all(config: dict, save_dir: str) -> dict: + """ + Train all algorithms sequentially and return combined results. + 按顺序训练所有算法并返回组合结果。 + + This facilitates large-scale comparison across all baselines. + 这有助于跨所有基准算法进行大规模比较。 + """ + all_results = {} + algorithms = ['co_maddpg', 'pure_coop', 'pure_comp', 'single_dqn', + 'iddpg', 'fixed_lambda', 'equal_alloc', 'semantic_only'] + + for algo_name in algorithms: + try: + history = train_single(algo_name, config, save_dir) + all_results[algo_name] = history + except Exception as e: + print(f"\n ERROR training {algo_name}: {e}") + import traceback + traceback.print_exc() + all_results[algo_name] = None + + # Save combined results | 保存组合结果 + combined_path = os.path.join(save_dir, 'all_results.json') + serializable = {} + for k, v in all_results.items(): + if v is None: + serializable[k] = None + else: + serializable[k] = { + kk: [float(x) for x in vv] if isinstance(vv, list) else float(vv) + for kk, vv in v.items() + } + with open(combined_path, 'w') as f: + json.dump(serializable, f, indent=2) + + print(f"\nAll results saved to: {combined_path}") + return all_results + + +def main(): + """ + Main entry point with CLI argument parsing. | 带有命令行参数解析的主入口。 + """ + parser = argparse.ArgumentParser(description='Co-MADDPG Training') + # Config arguments | 配置参数 + parser.add_argument('--config', type=str, default='configs/default.yaml', + help='Path to config YAML file') + parser.add_argument('--algorithm', type=str, default='co_maddpg', + choices=['co_maddpg', 'pure_coop', 'pure_comp', 'single_dqn', + 'iddpg', 'fixed_lambda', 'equal_alloc', 'semantic_only', 'all'], + help='Algorithm to train') + + # Override hyperparameters | 覆盖超参数 + parser.add_argument('--episodes', type=int, default=None, + help='Override max episodes') + parser.add_argument('--steps', type=int, default=None, + help='Override max steps per episode') + parser.add_argument('--seed', type=int, default=None, + help='Override random seed') + + # Resource / output settings | 资源 / 输出设置 + parser.add_argument('--save_dir', type=str, default=None, + help='Directory to save results') + parser.add_argument('--gpu', type=int, default=0, + help='GPU device index (-1 for CPU)') + + args = parser.parse_args() + + # Load config | 加载配置 + config_path = os.path.join(PROJECT_ROOT, args.config) + config = load_config(config_path) + + # Override config with CLI args | 用命令行参数覆盖配置 + if args.episodes is not None: + config['training']['max_episodes'] = args.episodes + if args.steps is not None: + config['training']['max_steps'] = args.steps + if args.seed is not None: + config['training']['seed'] = args.seed + + # Hardware selection | 硬件选择 + if args.gpu >= 0 and torch.cuda.is_available(): + torch.cuda.set_device(args.gpu) + print(f"Using GPU: {torch.cuda.get_device_name(args.gpu)}") + else: + print("Using CPU") + + # Create timestamped save directory | 创建带有时间戳的保存目录 + if args.save_dir: + save_dir = args.save_dir + else: + timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') + save_dir = os.path.join(PROJECT_ROOT, 'results', f'run_{timestamp}') + os.makedirs(save_dir, exist_ok=True) + + # Save a snapshot of the final config for reproducibility | 保存最终配置快照以确保可重复性 + config_snapshot_path = os.path.join(save_dir, 'config.yaml') + with open(config_snapshot_path, 'w') as f: + yaml.dump(config, f, default_flow_style=False) + + print(f"Config: {config_path}") + print(f"Save directory: {save_dir}") + print(f"Subcarriers: {config['env']['num_subcarriers']}") + print(f"Users: {config['env']['num_semantic_users']}S + {config['env']['num_traditional_users']}B") + print(f"Episodes: {config['training']['max_episodes']}") + print(f"Steps/episode: {config['training']['max_steps']}") + + # Start training | 开始训练 + if args.algorithm == 'all': + train_all(config, save_dir) + else: + train_single(args.algorithm, config, save_dir) + + print(f"\nDone! Results in: {save_dir}") + + +if __name__ == '__main__': + main() diff --git a/code/utils/__init__.py b/code/utils/__init__.py new file mode 100644 index 0000000..6b96a40 --- /dev/null +++ b/code/utils/__init__.py @@ -0,0 +1,10 @@ +# Utils initialization module +from .metrics import ( + jain_fairness, + rate_satisfaction, + compute_system_qoe, + compute_lambda, + compute_mixed_reward, + moving_average +) +from .visualization import Plotter diff --git a/code/utils/metrics.py b/code/utils/metrics.py new file mode 100644 index 0000000..7623237 --- /dev/null +++ b/code/utils/metrics.py @@ -0,0 +1,193 @@ +""" +Co-MADDPG Evaluation Metrics | Co-MADDPG 评估指标 + +This module provides the core performance metrics and reward calculation logic for +the cooperative-competitive multi-agent reinforcement learning (Co-MADDPG) framework +in hybrid semantic-traditional wireless resource allocation. + +本模块为混合语义-传统无线资源分配中的协作-竞争多智能体强化学习（Co-MADDPG）框架 +提供核心性能指标和奖励计算逻辑。 + +Key Metrics: +- Jain's Fairness Index / Jain 公平性指数 +- Rate Satisfaction Ratio / 速率满足率 +- System-level QoE / 系统级体验质量 +- Dynamic Cooperation Weight (λ) / 动态协作权重 (λ) +- Mixed Reward Mechanism / 混合奖励机制 + +Reference: +- "Dynamic Cooperative-Competitive Multi-Agent Reinforcement Learning for + Resource Allocation in Semantic-Traditional Hybrid Wireless Networks" +""" + +import numpy as np + + +def jain_fairness(values) -> float: + """ + Compute Jain's fairness index. | 计算 Jain 公平性指数。 + + Formula: J = (Σ x_i)² / (n · Σ x_i²) + 公式：J = (Σ x_i)² / (n · Σ x_i²) + + Returns 0.0 if all values are zero or empty. | 如果所有值均为零或为空，则返回 0.0。 + + Parameters + ---------- + values : array_like + Resource allocation or performance metrics (e.g., rates, QoE). + 资源分配或性能指标（例如：速率、QoE）。 + + Returns + ------- + float + Fairness index in range [1/n, 1.0]. + [1/n, 1.0] 范围内的公平性指数。 + """ + values = np.asarray(values, dtype=np.float64) + if len(values) == 0: + return 0.0 + sum_sq = np.sum(values ** 2) + # Avoid division by zero | 避免除以零 + if sum_sq == 0: + return 0.0 + # Calculate J index | 计算 J 指数 + return float(np.sum(values) ** 2 / (len(values) * sum_sq)) + + +def rate_satisfaction(rates, r_req: float) -> float: + """ + Fraction of users meeting minimum rate requirement. | 满足最小速率要求的用户比例。 + + Parameters + ---------- + rates : array_like + Per-user achievable rates. + 每个用户可达到的速率。 + r_req : float + Minimum rate requirement threshold (R_req). + 最小速率要求阈值 (R_req)。 + + Returns + ------- + float + Fraction in [0, 1]. + [0, 1] 范围内的比例。 + """ + rates = np.asarray(rates) + if len(rates) == 0: + return 1.0 + # Count how many users' rates exceed the requirement | 统计速率超过要求的用户数量 + return float(np.mean(rates >= r_req)) + + +def compute_system_qoe(qoe_list) -> float: + """ + Compute system-level QoE as mean of per-user QoE values. | 计算系统级 QoE，即用户 QoE 的平均值。 + + Parameters + ---------- + qoe_list : array_like + List of QoE values for all active users. + 所有活跃用户的 QoE 值列表。 + + Returns + ------- + float + Mean system QoE. + 平均系统 QoE。 + """ + if len(qoe_list) == 0: + return 0.0 + # Simple arithmetic mean | 简单算术平均值 + return float(np.mean(qoe_list)) + + +def compute_lambda(qoe_sys: float, beta: float = 5.0, + q_th: float = 0.6) -> float: + """ + Compute dynamic cooperation weight λ using sigmoid function. | 使用 Sigmoid 函数计算动态协作权重 λ。 + + λ(t) = 1 / (1 + exp(-β · (QoE_sys - Q_th))) + + Parameters + ---------- + qoe_sys : float + Current system-level QoE. + 当前系统级 QoE。 + beta : float + Steepness of the switching transition (β). + 切换过渡的陡峭程度 (β)。 + q_th : float + QoE threshold for cooperative behavior (Q_th). + 协作行为的 QoE 阈值 (Q_th)。 + + Returns + ------- + float + λ value in [0, 1], representing the degree of cooperation. + [0, 1] 范围内的 λ 值，代表协作程度。 + """ + # Sigmoid function maps QoE difference to [0, 1] | Sigmoid 函数将 QoE 差异映射到 [0, 1] + return float(1.0 / (1.0 + np.exp(-beta * (qoe_sys - q_th)))) + + +def compute_mixed_reward(qoe_self: float, qoe_other: float, + qoe_sys: float, lambda_val: float, + coop_w=(0.5, 0.3, 0.2), + comp_w=(0.8, 0.2)) -> float: + """ + Compute dynamically mixed cooperative-competitive reward. | 计算动态混合的协作-竞争奖励。 + + r = λ · r_coop + (1-λ) · r_comp + + Parameters + ---------- + qoe_self : float + Individual QoE of the agent. | 智能体自身的 QoE。 + qoe_other : float + Mean QoE of other agents in the same cell. | 同小区内其他智能体的平均 QoE。 + qoe_sys : float + Overall system QoE. | 系统整体 QoE。 + lambda_val : float + Dynamic cooperation weight (λ). | 动态协作权重 (λ)。 + coop_w : tuple + Weights for cooperative reward (self, others, system). | 协作奖励权重（自身、他人、系统）。 + comp_w : tuple + Weights for competitive reward (self, system). | 竞争奖励权重（自身、系统）。 + + Returns + ------- + float + The final mixed reward value. | 最终混合奖励值。 + """ + # Cooperative reward emphasizes global performance | 协作奖励强调全局性能 + r_coop = coop_w[0] * qoe_self + coop_w[1] * qoe_other + coop_w[2] * qoe_sys + # Competitive reward focuses more on individual gain | 竞争奖励更关注个人收益 + r_comp = comp_w[0] * qoe_self + comp_w[1] * qoe_sys + + # Linear combination based on lambda | 基于 lambda 的线性组合 + return float(lambda_val * r_coop + (1.0 - lambda_val) * r_comp) + + +def moving_average(values, window: int = 50) -> np.ndarray: + """ + Compute moving average of a series for visualization smoothing. | 计算序列的移动平均值，用于可视化平滑。 + + Parameters + ---------- + values : array_like + Input time series data. | 输入的时间序列数据。 + window : int + Smoothing window size. | 平滑窗口大小。 + + Returns + ------- + np.ndarray + Smoothed series. | 平滑后的序列。 + """ + values = np.asarray(values, dtype=np.float64) + if len(values) < window: + return values + # Standard 1D convolution for moving average | 用于移动平均的标准一维卷积 + return np.convolve(values, np.ones(window) / window, mode='valid') diff --git a/code/utils/visualization.py b/code/utils/visualization.py new file mode 100644 index 0000000..73f48ad --- /dev/null +++ b/code/utils/visualization.py @@ -0,0 +1,313 @@ +""" +Co-MADDPG Visualization Module | Co-MADDPG 可视化模块 + +This module handles the generation of IEEE-standard figures for the resource +allocation performance evaluation. It maps specifically to Section VII (Experimental +Results) of the associated research paper. + +本模块负责生成符合 IEEE 标准的资源分配性能评估图表。它专门对应于相关研究论文的 +第七节（实验结果）。 + +Reference Figures (from Section VII): +- Fig 2: Training convergence curves | 训练收敛曲线 +- Fig 3: System QoE vs. SNR | 系统 QoE 随 SNR 的变化 +- Fig 4: Jain's Fairness Index vs. SNR | Jain 公平性指数随 SNR 的变化 +- Fig 5: System QoE vs. Total Users (K) | 系统 QoE 随总用户数 (K) 的变化 +- Fig 6: Rate Satisfaction Ratio vs. Total Users (K) | 速率满足率随总用户数 (K) 的变化 +- Fig 7: Trajectory of λ(t) over time | λ(t) 随时间的变化轨迹 +- Fig 8: Correlation scatter of λ and System QoE | λ 与系统 QoE 的相关性散点图 +- Fig 9: System QoE vs. Semantic User Ratio | 系统 QoE 随语义用户比例的变化 +- Fig 10: Ablation study results | 消融实验结果 +- Fig 11: Sensitivity analysis of β | β 参数的敏感性分析 +- Fig 12: Sensitivity analysis of Q_th | Q_th 阈值的敏感性分析 +""" + +import os +import numpy as np +import matplotlib +# Use non-interactive backend to avoid requiring an X server or display +# 使用非交互式后端以避免需要 X 服务器或显示器 +matplotlib.use('Agg') +import matplotlib.pyplot as plt + +from utils.metrics import moving_average + +# IEEE-quality plotting defaults | IEEE 质量绘图默认设置 +plt.rcParams.update({ + 'font.family': 'serif', + 'font.serif': ['Times New Roman', 'DejaVu Serif'], + 'font.size': 12, + 'axes.grid': True, + 'figure.figsize': (8, 6), + 'figure.autolayout': True, # Equivalent to tight_layout | 等同于 tight_layout + 'savefig.dpi': 300, + 'savefig.bbox': 'tight' +}) + +# Consistent algorithm styles | 一致的算法绘图风格 +# Color and marker choices distinguish between proposed, baselines, and ablation variants +# 颜色和标记的选择用于区分建议算法、基准算法和消融变体 +ALGO_STYLES = { + 'Co-MADDPG': {'color': '#E24A33', 'marker': 'o', 'linestyle': '-'}, # Proposed (Red) | 建议算法（红色） + 'Pure Cooperative': {'color': '#348ABD', 'marker': 's', 'linestyle': '--'}, # Baseline (Blue) | 基准（蓝色） + 'Pure Competitive': {'color': '#988ED5', 'marker': '^', 'linestyle': '--'}, # Baseline (Purple) | 基准（紫色） + 'Single-Agent DQN': {'color': '#777777', 'marker': 'D', 'linestyle': '-.'}, # Baseline (Gray) | 基准（灰色） + 'IDDPG': {'color': '#FBC15E', 'marker': 'v', 'linestyle': '-.'}, # Baseline (Yellow) | 基准（黄色） + 'Fixed λ=0.5': {'color': '#8EBA42', 'marker': 'p', 'linestyle': ':'}, # Ablation (Green) | 消融（绿色） + 'Equal Allocation': {'color': '#FFB5B8', 'marker': '*', 'linestyle': ':'}, # Baseline (Pink) | 基准（粉色） + 'Semantic-Only': {'color': '#6d904f', 'marker': 'h', 'linestyle': ':'}, # Baseline (Olive) | 基准（橄榄色） +} + + +class Plotter: + """ + IEEE-quality plotting module for all paper figures. | 用于所有论文图表的 IEEE 质量绘图模块。 + """ + def __init__(self): + pass + + def _get_style(self, algo_name): + """ + Helper to get plotting style for an algorithm or a sensible default. + 获取算法的绘图风格或合理的默认值。 + """ + return ALGO_STYLES.get(algo_name, {'color': 'k', 'marker': '', 'linestyle': '-'}) + + def _save_plot(self, save_path): + """ + Helper to save plot in both PDF and PNG formats at 300 DPI. + 以 300 DPI 的分辨率将图表保存为 PDF 和 PNG 格式。 + """ + os.makedirs(os.path.dirname(os.path.abspath(save_path)), exist_ok=True) + # Strip extension if the user provided one, to consistently save both .pdf and .png + # 如果用户提供了扩展名，则将其去除，以便一致地保存 .pdf 和 .png + base_path = os.path.splitext(save_path)[0] + + plt.savefig(f"{base_path}.pdf", format='pdf') + plt.savefig(f"{base_path}.png", format='png', dpi=300) + plt.close() + + def plot_convergence(self, data_dict, save_path): + """ + Fig 2: Episode QoE_sys curves. | 图 2：每回合系统 QoE 曲线。 + Shows how the algorithm improves over training episodes. + 展示算法在训练回合中如何改进。 + + data_dict: {algo_name: [episode_qoe_values]} + """ + plt.figure() + for algo, qoe_vals in data_dict.items(): + style = self._get_style(algo) + # Remove markers for dense convergence plots to maintain clean look + # 为密集的收敛图移除标记以保持画面整洁 + plot_style = style.copy() + if 'marker' in plot_style: + plot_style.pop('marker') + + smoothed_qoe = moving_average(qoe_vals, window=50) + x_vals = np.arange(len(smoothed_qoe)) + plt.plot(x_vals, smoothed_qoe, label=algo, **plot_style) + + plt.xlabel('Episode') + plt.ylabel('System QoE') + plt.title('Training Convergence') + plt.legend() + self._save_plot(save_path) + + def plot_qoe_vs_snr(self, data_dict, save_path): + """ + Fig 3: QoE vs SNR. | 图 3：QoE 随 SNR 的变化。 + Evaluates system robustness under different noise levels. + 评估不同噪声水平下的系统鲁棒性。 + + data_dict: {algo_name: [qoe_per_snr_point]} + """ + plt.figure() + snr_vals = [0, 5, 10, 15, 20, 25, 30] + for algo, qoe_vals in data_dict.items(): + style = self._get_style(algo) + plt.plot(snr_vals, qoe_vals, label=algo, **style) + + plt.xlabel('SNR (dB)') + plt.ylabel('System QoE') + plt.title('System QoE vs. SNR') + plt.legend() + self._save_plot(save_path) + + def plot_fairness_vs_snr(self, data_dict, save_path): + """ + Fig 4: Jain Fairness Index vs SNR. | 图 4：Jain 公平性指数随 SNR 的变化。 + Measures the balance of resource allocation across users. + 衡量不同用户之间资源分配的平衡性。 + + data_dict: {algo_name: [fairness_per_snr_point]} + """ + plt.figure() + snr_vals = [0, 5, 10, 15, 20, 25, 30] + for algo, fairness_vals in data_dict.items(): + style = self._get_style(algo) + plt.plot(snr_vals, fairness_vals, label=algo, **style) + + plt.xlabel('SNR (dB)') + plt.ylabel('Jain Fairness Index') + plt.title('Fairness vs. SNR') + plt.legend() + self._save_plot(save_path) + + def plot_qoe_vs_users(self, data_dict, save_path): + """ + Fig 5: QoE vs Total Users K. | 图 5：QoE 随总用户数 K 的变化。 + Tests system scalability as user density increases. + 测试随着用户密度增加系统的可扩展性。 + + data_dict: {algo_name: [qoe_per_k_point]} + """ + plt.figure() + users_vals = [4, 6, 8, 10, 12] + for algo, qoe_vals in data_dict.items(): + style = self._get_style(algo) + plt.plot(users_vals, qoe_vals, label=algo, **style) + + plt.xlabel('Total Users (K)') + plt.ylabel('System QoE') + plt.title('System QoE vs. Total Users') + plt.legend() + self._save_plot(save_path) + + def plot_rate_satisfaction_vs_users(self, data_dict, save_path): + """ + Fig 6: Rate Satisfaction Ratio vs Total Users K. | 图 6：速率满足率随总用户数 K 的变化。 + Evaluates the ability to meet minimum QoS requirements. + 评估满足最小 QoS 要求的能力。 + + data_dict: {algo_name: [rate_satisfaction_per_k_point]} + """ + plt.figure() + users_vals = [4, 6, 8, 10, 12] + for algo, sat_vals in data_dict.items(): + style = self._get_style(algo) + plt.plot(users_vals, sat_vals, label=algo, **style) + + plt.xlabel('Total Users (K)') + plt.ylabel('Rate Satisfaction Ratio') + plt.title('Rate Satisfaction vs. Total Users') + plt.legend() + self._save_plot(save_path) + + def plot_lambda_trajectory(self, lambda_values, save_path): + """ + Fig 7: Lambda Trajectory. | 图 7：Lambda 轨迹。 + Visualizes the dynamic switching between cooperation and competition. + 可视化协作与竞争之间的动态切换。 + + lambda_values: list of lambda(t) values. + """ + plt.figure() + time_steps = np.arange(len(lambda_values)) + plt.plot(time_steps, lambda_values, label=r'$\lambda(t)$', color='#348ABD', linestyle='-') + # Reference line for fixed weighting | 固定权重的参考线 + plt.axhline(y=0.5, color='#E24A33', linestyle='--', label=r'Reference ($\lambda=0.5$)') + + plt.xlabel('Time Step') + plt.ylabel(r'$\lambda(t)$') + plt.title(r'Trajectory of Allocation Parameter $\lambda$') + plt.legend() + self._save_plot(save_path) + + def plot_lambda_qoe_scatter(self, lambdas, qoes, save_path): + """ + Fig 8: Scatter of (lambda, QoE_sys). | 图 8：(lambda, QoE_sys) 散点图。 + Shows the correlation between the dynamic parameter and system performance. + 展示动态参数与系统性能之间的相关性。 + """ + plt.figure() + time_steps = np.arange(len(lambdas)) + # Color points by time to show evolution | 按时间为点着色以显示演化过程 + sc = plt.scatter(lambdas, qoes, c=time_steps, cmap='viridis', alpha=0.7) + cbar = plt.colorbar(sc) + cbar.set_label('Time Step') + + plt.xlabel(r'$\lambda$') + plt.ylabel('System QoE') + plt.title(r'Correlation between $\lambda$ and System QoE') + self._save_plot(save_path) + + def plot_qoe_vs_ratio(self, data_dict, ratios, save_path): + """ + Fig 9: QoE vs Semantic User Ratio. | 图 9：QoE 随语义用户比例的变化。 + Studies the impact of increasing semantic communication prevalence. + 研究语义通信普及率增加的影响。 + + data_dict: {algo_name: [qoe_values]} + """ + plt.figure() + for algo, qoe_vals in data_dict.items(): + style = self._get_style(algo) + plt.plot(ratios, qoe_vals, label=algo, **style) + + plt.xlabel('Semantic User Ratio') + plt.ylabel('System QoE') + plt.title('System QoE vs. Semantic User Ratio') + plt.legend() + self._save_plot(save_path) + + def plot_ablation(self, data, save_path): + """ + Fig 10: Horizontal bar chart for ablation study. | 图 10：消融研究的水平条形图。 + Compares the full Co-MADDPG against its stripped-down variants. + 将完整的 Co-MADDPG 与其简化变体进行比较。 + + data: {variant_label: qoe_value} + """ + plt.figure() + labels = list(data.keys()) + values = list(data.values()) + + y_pos = np.arange(len(labels)) + # Highlight Co-MADDPG (Full) in red if present | 如果存在，用红色高亮 Co-MADDPG (Full) + colors = ['#E24A33' if 'Co-MADDPG' in label and 'Full' in label else '#348ABD' for label in labels] + + plt.barh(y_pos, values, align='center', color=colors) + plt.yticks(y_pos, labels) + plt.xlabel('System QoE') + plt.title('Ablation Study') + + self._save_plot(save_path) + + def plot_beta_sensitivity(self, data_dict, betas, save_path): + """ + Fig 11: QoE vs Beta values. | 图 11：QoE 随 Beta 值的变化。 + Analyzes sensitivity to the sigmoid steepness parameter. + 分析对 Sigmoid 陡峭度参数的敏感性。 + + data_dict: {label: qoe_value_list} + """ + plt.figure() + for algo, qoe_vals in data_dict.items(): + style = self._get_style(algo) + plt.plot(betas, qoe_vals, label=algo, **style) + + plt.xlabel(r'$\beta$ Parameter') + plt.ylabel('System QoE') + plt.title(r'Sensitivity Analysis of $\beta$') + plt.legend() + self._save_plot(save_path) + + def plot_qth_sensitivity(self, data_dict, qths, save_path): + """ + Fig 12: QoE vs Q_th values. | 图 12：QoE 随 Q_th 值的变化。 + Analyzes sensitivity to the cooperation threshold. + 分析对协作阈值的敏感性。 + + data_dict: {label: qoe_value_list} + """ + plt.figure() + for algo, qoe_vals in data_dict.items(): + style = self._get_style(algo) + plt.plot(qths, qoe_vals, label=algo, **style) + + plt.xlabel(r'$Q_{th}$ Threshold') + plt.ylabel('System QoE') + plt.title(r'Sensitivity Analysis of $Q_{th}$') + plt.legend() + self._save_plot(save_path) diff --git a/extraction_log.txt b/extraction_log.txt new file mode 100644 index 0000000..f80c0f4 --- /dev/null +++ b/extraction_log.txt @@ -0,0 +1,10 @@ +Processing: Noh 等 - Deep Reinforcement Learning-Based Resource Allocation and Mode Selection for Semantic Communication.pdf + Pages: 6 + Written paper1.txt: 6 pages extracted +Processing: Xie 等 - 2025 - Hybrid Digital-Analog Semantic Communications.pdf + Pages: 15 + Written paper2.txt: 15 pages extracted +Processing: Zhang 等 - 2026 - Resource Allocation in Wireless Semantic Communications A Comprehensive Survey.pdf + Pages: 37 + Written paper3.txt: 37 pages extracted +DONE diff --git a/paper/01_introduction.md b/paper/01_introduction.md new file mode 100644 index 0000000..197fe84 --- /dev/null +++ b/paper/01_introduction.md @@ -0,0 +1,30 @@ +# 第一章：引言 (Introduction) + +## 1.1 研究背景 +随着第六代移动通信 (6G) 愿景的提出，无线通信网络正从单纯的“连接万物”向“连接智能”演进 [1]。传统通信系统主要基于香农 (Shannon) 经典信息论 [8]，通过提高传输功率、扩展频谱带宽或采用高阶调制等手段来追求数据传输速率的极限。然而，在频谱资源日益稀缺且通信需求呈爆炸式增长的背景下，传统比特级传输正面临严峻的能效与谱效瓶颈。 + +语义通信 (Semantic Communication, SemCom) 作为一种颠覆性的通信范式，通过提取并传输信息的深层含义而非原始比特，实现了远超传统通信的数据压缩率 [1], [4]。这种以任务为导向、以语义为核心的传输模式，能够显著降低带宽消耗，被公认为支撑未来超大规模智能应用的关键技术。尽管如此，在可预见的未来，语义通信无法完全取代基于比特传输的传统通信。在同一个正交频分多址 (OFDMA) 系统中，支持多媒体检索、智能监控等任务的语义用户 (Semantic Users) 必将与执行文件下载、网页浏览等任务的传统用户 (Bit-stream Users) 长期共存。这种共存环境下的资源共享问题，是实现语义通信落地应用的核心挑战。 + +## 1.2 现有工作的不足 +针对语义通信的资源管理，学术界已开展了一系列探索。例如，研究者提出了基于深度强化学习的模式选择方案 [2]，以及针对混合数模语义系统的带宽分配算法 [3]。然而，现有工作在处理语义通信与传统通信的共存关系时，仍存在以下显著局限性： + +1. **单一博弈模式的局限**：现有研究通常将两类用户的交互简化为纯合作 (Cooperative) 或纯竞争 (Competitive) 模式 [4]。纯合作模式假设所有智能体完全无私地最大化系统总效用，往往忽略了不同业务实体的利益诉求；而纯竞争模式虽反映了资源的稀缺性，却常导致局部最优甚至系统崩溃，造成严重的全局低效。 +2. **缺乏灵活性切换机制**：在动态时变的无线环境下，系统对实时性、可靠性和体验质量 (Quality of Experience, QoE) 的要求随时间波动。现有的静态资源分配方案无法根据系统整体 QoE 的变化，在合作与竞争之间进行平滑且灵活的策略切换。 +3. **层级关系建模缺失**：由于语义通信通常具有更高的智能程度和任务复杂性，两类通信实体在决策顺序和影响力上表现出明显的非对称性。现有工作大多采用对称博弈，难以刻画这种天然的层级依存关系。 + +## 1.3 研究动机 +为了克服上述局限，本研究引入了合作竞争 (Coopetition) 理论 [6]。合作竞争并非简单的两者择一，而是一种在竞争中寻求合作、在合作中保持竞争的混合博弈框架。在 OFDMA 系统中，$K_s$ 个语义智能体 (Agent_S) 与 $K_b$ 个传统智能体 (Agent_B) 共享 $N$ 个子载波。这种场景天然具备合作竞争的特征：一方面，双方为了最大化各自的 QoE 而在频谱资源上展开竞争；另一方面，为了维持系统的整体稳定性并避免极端的干扰，双方又必须在资源调度上达成某种程度的协作。 + +引入合作竞争框架的动机在于：通过动态调整合作与竞争的权重 $\lambda(t)$，系统可以根据当前的运行状态自适应地平衡个体利益与集体效用。当系统整体体验质量 $QoE_{sys}$ 较低时，增强合作分量以保障基本通信服务；当 $QoE_{sys}$ 处于较高水平时，释放竞争活力以激发各智能体的性能潜力。这种机制不仅能提升系统的稳健性，还能通过更精细的博弈建模提高资源的利用效率。 + +## 1.4 主要贡献 +本文针对语义与传统通信共存场景下的资源分配问题，提出了基于合作竞争多智能体强化学习的优化方案。具体贡献概括如下： + +* **(C1) 首次将合作竞争博弈引入语义与传统通信共存场景**：针对 OFDMA 系统的子载波与功率分配问题，建立了显式的合作竞争博弈模型。该模型突破了传统单一博弈模式的限制，为语义通信与传统通信的异构资源共享提供了全新的理论视角。 +* **(C2) 提出层级 Stackelberg 博弈建模**：考虑两类通信实体的智能程度差异，将语义智能体设定为领导者 (Leader)，传统智能体设定为跟随者 (Follower)。通过刻画这种非对称的策略交互，更准确地描述了复杂无线环境中的资源博弈过程。 +* **(C3) 设计基于系统 QoE 的动态自适应切换机制**：提出了一种随时间变化的合作竞争切换因子 $\lambda(t)$。该因子根据实时反馈的 $QoE_{sys}$ 自动调整奖励函数中的合作权重，实现了系统性能与个体需求的动态平衡。 +* **(C4) 提出 Co-MADDPG 算法框架**：基于集中式训练、分布式执行 (CTDE) 架构，设计了合作竞争多智能体深度确定性策略梯度 (Co-MADDPG) 算法 [5]。该算法通过引入 $\lambda(t)$ 修饰的混合奖励机制，有效解决了混合博弈环境下的非平稳性问题。 +* **(C5) 严密的理论证明与仿真验证**：从数学上证明了所提博弈模型中 Stackelberg 均衡 (SE) 的存在性，推导了合作竞争带来的性能增益下界，并对 $\lambda(t)$ 的收敛性以及算法的稳定性进行了理论分析。仿真结果验证了所提方案在提升频谱效率和用户公平性方面的优越性。 + +## 1.5 论文组织结构 +本文的其余部分组织如下。第二节回顾了语义通信资源分配、合作竞争博弈理论和多智能体强化学习的相关工作。第三节描述了 OFDMA 系统模型，包括网络架构、信道模型、语义与传统通信模型以及统一的 QoE 指标。第四节建立联合优化问题并引入 Stackelberg 合作竞争博弈建模与动态 $\lambda(t)$ 切换机制。第五节提供严格的理论分析，包括均衡存在性、合作竞争增益下界、$\lambda(t)$ 收敛性和算法收敛性。第六节详细阐述所提出的 Co-MADDPG 算法设计与伪代码。第七节展示仿真结果与性能评估。最后，第八节总结全文并探讨未来的研究方向。 \ No newline at end of file diff --git a/paper/02_related_work.md b/paper/02_related_work.md new file mode 100644 index 0000000..bb25d2d --- /dev/null +++ b/paper/02_related_work.md @@ -0,0 +1,48 @@ +# 第二章：相关工作 (Related Work) + +本章旨在系统回顾语义通信资源分配、合作竞争博弈论以及多智能体深度强化学习 (MARL) 的最新研究进展，并以此识别现有工作中的研究空白。 + +## A. 语义通信中的资源分配 (Resource Allocation in Semantic Communications) + +语义通信作为一种超越香农范式的通信架构，正逐渐从理论模型向实际部署演进。自 Xie 和 Qin 提出 DeepSC [1] 框架以来，语义通信的系统设计已取得了显著突破。DeepSC 及其后续变体通过联合信源信道编码 (JSCC)，将传统物理层传输转变为基于文本或图像语义的任务。然而，由于语义特征的大小与信源内容紧密耦合，其资源需求呈现出高度的动态性。 + +针对语义通信环境下的资源调度，Noh 等人 [2] 提出了一种基于深度 Q 网络 (DQN) 的单智能体资源分配方法，探讨了在多模态场景下的模式选择与带宽分配问题。此工作展示了深度强化学习在处理复杂语义指标（如语义相似度）方面的潜力，但其假设单一接入点控制所有资源，未考虑多用户间的交互。Xie 等人 [3] 进一步扩展了应用范围，提出了 HDA-DeepSC 框架，实现了数字与模拟混合的语义通信系统，旨在提高不同信道条件下的传输鲁棒性。尽管技术不断迭代，Zhang 等人 [4] 在其综述中指出，目前的主流方法可分为基于传统优化、基于学习以及混合驱动三类。 + +现有研究的一个共同局限在于，大多数工作假设语义通信设备独占系统资源，或者与传统通信用户进行简单的正交分配 (Orthogonal Allocation)。这种静态划分忽略了语义业务与传统比特业务在共存环境下的深度交互，未能解决如何在有限频谱内实现两种异构业务的高效统筹。 + +## B. 合作竞争博弈理论 (Cooperative-Competitive Game Theory) + +在多用户无线网络中，用户间的交互通常具有合作与竞争的双重属性，这种动态关系可通过合作竞争 (Coopetition) 理论进行建模。Brandenburger 和 Nalebuff [6] 奠定了 Coopetition 的理论基础，指出主体可以通过合作扩大总价值，同时通过竞争分配利润。这种思想在无线通信中有着广泛的应用背景。 + +Stackelberg 博弈是处理层级化竞争的经典工具，已被大量应用于基站与用户间的功率控制及频谱租用。然而，普通的非合作博弈往往会导致纳什均衡点偏离系统最优。Parzy 和 Bogucka [7] 的研究是与本文最相关的先验工作，他们将 Coopetition 引入 OFDMA 认知无线电网络，通过混合效能函数平衡了各用户的独立目标与系统整体性能。虽然该项工作验证了合作竞争博弈在传统比特流传输中的有效性，但其尚未触及语义通信这一维度。 + +传统的博弈论方法，如纳什议价 (Nash Bargaining) 或联盟博弈，在处理高度非平稳的大规模网络时面临计算复杂度爆炸的挑战。此外，现有博弈模型大多基于固定的合作权重，无法根据实时的业务需求（如语义任务的紧急程度）自适应地调整用户间的相互关系。 + +## C. 多智能体深度强化学习 (Multi-Agent Deep Reinforcement Learning) + +随着智能边缘设备数量的激增，多智能体深度强化学习 (MARL) 成为解决分布式资源分配的核心技术。Lowe 等人 [5] 提出的 MADDPG 算法通过集中式训练与分布式执行 (CTDE) 架构，有效地缓解了多智能体环境中的非平稳性问题。 + +在传统的 Independent DQN 或 DDPG 方法中，由于每个智能体将其他参与者视为环境的一部分，导致训练过程极难收敛。相比之下，CTDE 框架允许智能体在训练阶段共享全局状态信息，从而学习更复杂的协调策略。在混合合作竞争任务中，近期出现了一些代表性进展。例如，SoLPO [9] 在自动驾驶领域引入了社交奖励 (Social Reward) 机制，通过引导智能体关注其对他者的影响来促进安全协作。Yang 等人 [10] 则探索了 Stackelberg-MADDPG，将层级博弈结构嵌入到神经网络的更新过程中。 + +尽管 MARL 在无人机通信、车联网资源调度中得到了应用，但针对语义通信特有指标（如语义熵、重建保真度）的优化方案仍处于起步阶段。尤其是如何在奖励函数设计中显式地体现合作竞争的动态平衡，依然是当前学术界的一个重要挑战。 + +## D. 差距分析与本文定位 (Research Gap and Our Positioning) + +通过对上述文献的梳理，可以发现语义通信与合作竞争博弈的结合仍是一个尚未被充分开发的领域。下表总结了本文工作与现有代表性研究的对比： + +| 工作 | 语义通信 | 传统通信 | 合作 | 竞争 | 动态切换 | MARL | 理论保证 | +|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +| Noh et al. [2] | ✓ | ✓ | — | — | — | Single DQN | — | +| Xie et al. [3] | ✓ | — | — | — | — | — | — | +| Parzy & Bogucka [7] | — | ✓ | ✓ | ✓ | 固定 | — | 部分 | +| MADDPG [5] | — | — | ✓ | ✓ | — | ✓ | — | +| SoLPO [9] | — | — | ✓ | ✓ | 社交奖励 | ✓ | — | +| **本文 (Ours)** | **✓** | **✓** | **✓** | **✓** | **自适应λ(t)** | **✓** | **✓** | + +本文的定位在于填补以下四个关键领域的研究空白： +1. **异构业务共存**：首次在统一的资源分配框架下，显式地处理语义通信用户与传统通信用户的资源竞争与相互补偿。 +2. **显式合作竞争动态**：不同于传统的固定效用函数，本文引入了受博弈论启发的社交倾向性指标，表征用户间的策略交互。 +3. **自适应切换机制**：通过学习一个随时间演进的权重系数 $\lambda(t)$，智能地在纯竞争行为与协作行为之间平衡，以应对多变的信道状态。 +4. **理论与实践结合**：在利用 MARL 实现高效分布式执行的同时，通过数学证明给出了系统稳定性的理论边界。 + +综上所述，本文的研究工作不仅是对现有语义通信资源分配方法的补充，更是将博弈论与分布式人工智能深度融合的一次重要尝试。这种全新的视角将为未来 6G 网络中的异构业务支持提供重要的理论指导。 \ No newline at end of file diff --git a/paper/03_system_model.md b/paper/03_system_model.md new file mode 100644 index 0000000..7cbebcb --- /dev/null +++ b/paper/03_system_model.md @@ -0,0 +1,166 @@ +# 第三章：系统模型 (System Model) + +在本章中，我们将详细介绍面向语义-比特混合通信的单小区下行正交频分多址接入（Orthogonal Frequency Division Multiple Access, OFDMA）系统模型。系统建模主要包括网络拓扑与物理资源设定、射频空间信道衰落特征、异构流量的通信物理过程，以及适用于混合架构的统一用户体验质量（Quality of Experience, QoE）评估指标。通过建立严密且符合物理规律的数学模型，我们为后续章节中的多维资源联合分配与优化算法设计奠定坚实的理论基础。 + +## A. 网络模型 (Network Model) + +考虑一个典型的单小区下行链路 OFDMA 通信系统。在系统的几何中心部署了一个配置单天线的宏基站（Base Station, BS），旨在为覆盖范围内的 $K$ 个单天线移动用户设备提供下行数据传输与智能信息交互服务。 + +[图1: 语义-比特混合通信单小区下行链路 OFDMA 系统模型示意图] + +在这个异构网络中，为了适应未来 6G 网络中极为多样化的业务需求，我们将接入网络的用户划分为两类不同的群体： +第一类为**传统比特通信用户**（Bit-based Communication Users），其数量记为 $K_b$。此类用户主要承载对数据完整性和精确度要求极高的传统数字流，例如底层控制信令、精密仪器遥测数据或传统的可执行文件下载。 +第二类为**语义通信用户**（Semantic Communication Users），其数量记为 $K_s$。此类用户在终端设备上执行特定的人工智能任务（如自然语言理解、智能对话或图像识别），它们利用基于深度神经网络的语义收发机架构提取并传输信息的核心“语义”，而无需像传统通信那样逐比特精确恢复原始的底层二进制数据。 + +系统服务中的总用户数满足 $K = K_s + K_b$。我们定义传统比特通信用户集合为 $\mathcal{K}_b = \{1, 2, \dots, K_b\}$，语义通信用户集合为 $\mathcal{K}_s = \{K_b+1, K_b+2, \dots, K\}$，系统中所有处于活跃状态的用户总集合则记为 $\mathcal{K} = \mathcal{K}_b \cup \mathcal{K}_s$。 + +在物理资源层面，系统在射频频段拥有总带宽为 $B$ 的可用可用频谱资源。该连续频段被均匀且不重叠地划分为 $N$ 个相互正交的子载波（Subcarriers），构成系统的子载波集合 $\mathcal{N} = \{1, 2, \dots, N\}$。每个子载波的物理带宽（即子载波间隔）均为 $\Delta f = B/N$。 + +为了进行精细化的频域资源调度，我们定义一个二元指示变量 $\alpha_{k,n} \in \{0, 1\}$ 来表示特定子载波的分配状态： +$$ +\alpha_{k,n} = \begin{cases} +1, & \text{如果子载波 } n \text{ 被分配给用户 } k \\ +0, & \text{否则} +\end{cases} \quad (1) +$$ +公式(1)通过离散的数学形式表达了网络在频域的资源调度决策。为了保证不同用户之间在频域上保持绝对正交，从而在理论上完全消除同小区内的用户间干扰（Inter-User Interference, IUI），我们对资源调度过程施加独占性的子载波分配约束： +$$ +\sum_{k \in \mathcal{K}} \alpha_{k,n} \leq 1, \quad \forall n \in \mathcal{N} \quad (2) +$$ +公式(2)的物理含义非常明确：在任何一个正交频分复用的时隙内，全系统中的每一个子载波 $n$ 最多只能分配给唯一一个用户进行数据传输，同时该子载波也被允许保留在未分配的静默闲置状态。 + +此外，基站侧受限于射频前端硬件（如功率放大器线性区）和热功耗的物理条件，具有严格的最大发射功率限制，记为 $P_{\max}$。令 $p_{k,n}$ 表示基站在子载波 $n$ 上向目标用户 $k$ 发送无线信号时所分配的实际发射功率，发射功率必须满足非负性： +$$ +p_{k,n} \geq 0, \quad \forall k \in \mathcal{K}, n \in \mathcal{N} \quad (3) +$$ +该公式表示基站向任何载波注入的射频能量不能为负值。同时，基站在整个工作频带上分配的功率总和绝不能超过其物理允许的阈值上限，即总功率约束： +$$ +\sum_{k \in \mathcal{K}} \sum_{n \in \mathcal{N}} p_{k,n} \leq P_{\max} \quad (4) +$$ +公式(4)反映了基站侧总发射能量是受限的稀缺资源，需要在不同用户和各频带之间进行合理的均衡分配以实现系统效用最大化。 + +## B. 信道模型 (Channel Model) + +本小节阐述无线射频信号在从基站天线传播至各远端用户过程中的信道衰落物理特性。为了准确刻画高度拥挤且遮挡严重的现代化城市通信环境，我们采用 3GPP 国际标准化组织定义的城市微蜂窝（Urban Micro, UMi）信道模型来计算信号的大尺度路径损耗。 + +记系统中用户 $k$ 到中央基站的三维欧式物理直线距离为 $d_k$（单位为米）。信号在自由空间与建筑物间反射衍射所经历的综合路径损耗（Path Loss）$\text{PL}(d_k)$ 以分贝（dB）为物理单位，可以表示为： +$$ +\text{PL}(d_k) = 36.7 \log_{10}(d_k) + 22.7 + 26\log_{10}(f_c) \quad (5) +$$ +其中，$f_c$ 表示通信系统的中心载波频率（单位为 GHz）。公式(5)定量刻画了高频电磁波在复杂城市建筑群和街道峡谷中传播时，由于距离延长和载频升高而导致的确定性大规模能量耗散规律。 + +在微观层面，考虑到丰富的多径效应（Multipath Effect）会导致显著的小尺度衰落（Small-scale Fading），我们将基站到用户 $k$ 在子载波 $n$ 上的复数信道状态增益记为 $h_{k,n}$。由于在城市密集区通常不存在基站与用户间的完美视距路径（Non-Line-of-Sight, NLOS），我们建模小尺度衰落服从典型的瑞利（Rayleigh）分布，因此 $h_{k,n}$ 被表征为一个零均值的复高斯随机变量： +$$ +h_{k,n} \sim \mathcal{CN}(0, 10^{-\text{PL}(d_k)/10}) \quad (6) +$$ +公式(6)表明，小尺度信道衰落系数 $h_{k,n}$ 对应的信号方差（即平均信道能量增益）等同于经过大尺度路径损耗衰减后的平均剩余能量。这为物理层的包络畸变提供了概率学基础。 + +**Assumption 1 (块衰落信道假设):** 在我们的系统建模中，假设多径无线信道表现为准静态的块衰落（Block Fading）特性。这意味着在每一个资源调度周期的时隙（Time Slot）内，所有活跃用户在所有子载波上的信道状态信息（Channel State Information, CSI）保持绝对恒定，但在不同时隙之间会根据瑞利分布发生独立的随机更迭。 + +在信号接收端，用户 $k$ 的射频前端在子载波 $n$ 上接收到的有用信号不可避免地会受到加性高斯白噪声（Additive White Gaussian Noise, AWGN）的污染。基于热噪声物理机制，该子载波频段上的本地噪声功率为： +$$ +\sigma^2 = N_0 \Delta f \quad (7) +$$ +其中 $N_0$ 表示接收机射频前端有效热噪声的功率谱密度（Power Spectral Density, PSD）。公式(7)说明了在带宽一定的子载波内所累积的本底噪声功率与其子频带宽度之间呈现严格的正比例关系。 + +综合上述传播规律与噪声机理，当基站以功率 $p_{k,n}$ 经由子载波 $n$ 向目标用户 $k$ 发送调制信号时，用户接收端在基带处理前的接收信噪比（Signal-to-Noise Ratio, SNR）数学定义为： +$$ +\gamma_{k,n} = \frac{p_{k,n} |h_{k,n}|^2}{\sigma^2} \quad (8) +$$ +公式(8)的物理含义极为核心：它表示接收端真实获取的有用信号功率（由发射功率通过衰落信道后决定）与底层背景热噪声功率的绝对比值，该参数直接决定了物理层在特定频点上无误传输数据的潜力与鲁棒性。 + +## C. 通信模型 (Communication Models) + +考虑到异构网络中两类用户呈现出截然不同的数据处理范式和应用需求，我们有必要分别建立各自独立的通信理论模型，以区分香农无损比特传输与深度学习语义特征传输在物理链路层面的差异。 + +### C.1 传统比特通信模型 (Traditional Bit Communication Model) + +对于属于集合 $\mathcal{K}_b$ 的传统比特通信用户，系统严格遵循经典香农信息论的基本准则，致力于实现无误差的底层比特流传输。在离散的 OFDMA 架构下，用户 $k \in \mathcal{K}_b$ 通过汇聚基站分配给它的所有正交子载波上的传输容量，其在当前时隙内的可达数据传输速率（Achievable Data Rate）计算如下： +$$ +R_k^{(b)} = \sum_{n=1}^{N} \alpha_{k,n} \Delta f \log_2(1 + \gamma_{k,n}) \quad (9) +$$ +公式(9)表示传统用户的总网络下行速率是其所独占的各个子载波上的香农信道容量的线性叠加，其物理含义是在现有的无线电波环境和功率分配下，单位时间内系统能够为该类用户进行无失真传输的最大理论比特数量。 + +同时，由于传统用户通常承载不可容忍长期拥塞的刚性数据业务（如自动驾驶控制指令或高实时后台同步数据），网络必须保障其最基础的通信服务质量。为此，每一个传统用户 $k \in \mathcal{K}_b$ 都向基站申报一个最低下行速率需求 $R_k^{\text{req}}$。倘若通过分配带来的实际速率 $R_k^{(b)}$ 无法跨越该刚性阈值，将直接导致该用户的上层业务连接中断或产生不可逆的超时故障。 + +### C.2 语义通信模型 (Semantic Communication Model) + +与传统通信专注于僵化地精准重建每一个二进制比特不同，处于集合 $\mathcal{K}_s$ 中的语义通信用户重点关注信息源中隐含的“含义”或“意图”在接收端的成功传递与复现。在这里，我们引入了典型的端到端联合信源-信道编码（Joint Source-Channel Coding, JSCC）神经架构——这一架构大量参考了业内广为接受的 DeepSC [1] 范式——专门用来对非结构化的语义特征进行深度的表征与映射。 + +具体流程而言，考虑基站当前需要向目标语义用户 $k$ 发送一段包含特定含义的源文本信息向量 $\mathbf{s}$。在发射端基带阶段，基站不再使用传统的信源和信道编码级联模块，而是利用一个预先在海量语料库上训练成熟的深度学习语义编码器 $f_{\theta_e}(\cdot)$（其中 $\theta_e$ 为该神经网络的可学习权重参数），将原始文本序列直接映射为物理层传输所需的连续复数域语义符号向量： +$$ +\mathbf{x} = f_{\theta_e}(\mathbf{s}) \in \mathbb{C}^{L_s} \quad (10) +$$ +公式(10)定义了神经网络参与的联合语义编码过程，其物理含义是通过多层自注意力机制提取源文本在语法树层面的深层网络特征，并将其密集地映射为适合在无线信道波形上调制的复基带符号 $\mathbf{x}$，其中所生成的符号长度维度被记为 $L_s$。 + +该语义符号向量 $\mathbf{x}$ 在越过空口进行传输时，必然会经历瑞利信道多径衰落和热噪声的干扰，最终抵达终端用户天线。用户端的复基带接收信号 $\hat{\mathbf{x}}$ 可以用线性代数方程表示为： +$$ +\hat{\mathbf{x}} = \mathbf{H} \mathbf{x} + \mathbf{n} \quad (11) +$$ +在该线性模型中，$\mathbf{H}$ 表示相应的对角信道衰落增益矩阵，$\mathbf{n} \sim \mathcal{CN}(\mathbf{0}, \sigma^2 \mathbf{I})$ 则表示空间中独立同分布的加性高斯白噪声向量。公式(11)生动地描述了高维语义符号在恶劣物理传输介质中受损与畸变的自然物理过程。 + +在终端侧，目标用户 $k$ 捕获到失真的符号序列 $\hat{\mathbf{x}}$ 之后，调用与基站联合配对优化的语义解码器 $f_{\theta_d}(\cdot)$（参数矩阵为 $\theta_d$）尝试穿透噪声干扰并重建源信息的初始语义内容，最终输出恢复的估计文本序列 $\hat{\mathbf{s}}$： +$$ +\hat{\mathbf{s}} = f_{\theta_d}(\hat{\mathbf{x}}) \quad (12) +$$ +公式(12)反映了基于深度网络前向传播的语义解码逆映射过程，即从含有不可避免噪声的物理空间复数特征中，重新抽取出具有人类逻辑理解意义的离散词汇序列。 + +为了科学量化这类智能语义传输范式的实际准确性，我们摒弃了缺乏语义表征能力的传统误比特率（BER），转而采纳更为先进的句子级语义相似度（Sentence Similarity, SSim）作为语义保真度（Semantic Fidelity）的核心评判度量。SSim 评分借助目前占据主导地位的预训练自然语言处理庞大模型（例如 BERT 模型网络），抽取源文本 $\mathbf{s}$ 和重建出的文本 $\hat{\mathbf{s}}$ 的高维句法嵌入向量表示，并计算此两者在多维流形空间内的余弦相似度夹角： +$$ +\text{SSim}(\mathbf{s}, \hat{\mathbf{s}}) = \frac{\mathcal{E}(\mathbf{s}) \cdot \mathcal{E}(\hat{\mathbf{s}})}{\|\mathcal{E}(\mathbf{s})\| \|\mathcal{E}(\hat{\mathbf{s}})\|} \quad (13) +$$ +其中函数 $\mathcal{E}(\cdot)$ 充当标准化的语句特征提取算子。公式(13)具有高度直观的物理意义：其计算结果的分值域严格限定在 $[0,1]$ 之间，得分越逼近自然数 1，说明源头意图与终端解码的语篇在人类或机器的高阶认知层面上越加吻合，即便某些具体的字面单词并不全然一致（例如“轿车”与“汽车”的同义替换）。 + +与传统通信相比，语义系统在架构上的一大颠覆性优势在于：它能够剔除语境冗余并对重要知识进行极度浓缩，从而显著降低空口资源所承载的符号长度。为此，我们正式定义语义压缩比（Semantic Compression Ratio）物理量 $\rho$： +$$ +\rho = \frac{L_s}{L_{\text{raw}}} \quad (14) +$$ +其中分母 $L_{\text{raw}}$ 是在同样的下行传输任务中，若采用香农传统通信范式（通过如 Huffman 熵编码加上诸如 QAM 调制等手段）将相同文本 $\mathbf{s}$ 转化为等效物理复数符号所需的总数量标准线。公式(14)衡量了新式语义编码器对于无线信道资源占用的相对压缩强度。在物理意义上，$\rho$ 越趋向于 0，说明神经网络的特征蒸馏越剧烈，消耗的空口时间或频带资源越少，频谱效率提升越显著。 + +**关键建模 (Critical Modeling of Semantic Performance)：** +由于基于多层非线性映射构建的深度学习系统表现如同缺乏明确内生表达式的黑盒结构，想要如同香农极限那样推导出精确解析解是一项近乎不可能的学术挑战。然而，通过海量蒙特卡洛仿真实验和真实数据集回归结果（例如 DeepSC 的开源测试），业界已形成明确共识：在网络拓扑确定的情况下，语义用户最终获取的语义相似度体验水平 $\text{SSim}$ 主要由两项综合性物理变量支配——网络底层的等效信噪比水平以及模型应用端主动选择的语义压缩比。 + +令 $\mathcal{N}_k = \{n \in \mathcal{N} \mid \alpha_{k,n} = 1\}$ 表示基站调度器分配给特定语义用户 $k$ 的正交子载波全集，利用符号 $|\mathcal{N}_k|$ 来表示该集合中所包含元素的基数大小。借此，我们可以定义用户 $k$ 经历的宏观平均信噪比 $\bar{\gamma}_k$ 为： +$$ +\bar{\gamma}_k = \frac{1}{|\mathcal{N}_k|} \sum_{n \in \mathcal{N}_k} \gamma_{k,n} \quad (15) +$$ +公式(15)的物理意义是将频域内分散且处于不同独立深衰落状态的各个子载波上的信道质量进行统计学平滑聚合，从而用一个标量客观代表整个语义数据传输批次（Batch）内所面对的综合等效无线传输环境恶劣度。 + +基于上述聚合参量，用户 $k$ 期望实现的句子级语义相似度即被创造性地抽象化为一个具有两项关键自变量（即平均信噪比 $\bar{\gamma}_k$ 与主动控制压缩比 $\rho_k$）的多元连续可导经验函数： +$$ +\text{SSim}_k = \phi(\bar{\gamma}_k, \rho_k) \quad (16) +$$ +为了支撑可追溯的凸或非凸数学优化推理，通过对诸如 DeepSC 等神经网络架构在带有高斯噪声与深度瑞利衰落的合成信道下测试的仿真性能数据图表进行高度密集的非线性曲线拟合回归，我们可以采用带有自由度参数控制的通用 Sigmoid 修正变体形式来进行高精度的数学解析近似： +$$ +\phi(\bar{\gamma}_k, \rho_k) = 1 - e^{-a(\rho_k) \cdot (\bar{\gamma}_k)^{b(\rho_k)}} \quad (17) +$$ +公式(17)完美捕捉了语义信息恢复在随信道状态波动下的特征曲线——典型的“瀑布型下坠效应”。从物理演化规律来看：当可用信噪比水平 $\bar{\gamma}_k$ 充沛并趋近于极大值时，接收端的语义相似度指数将无限次逼近其物理极限 1；然而当由于干扰导致信噪比退化时，信号解析性能开始呈现类似指数式的非线性恶化断崖下降。 +在这一核心公式内部，经验系数 $a(\rho_k) > 0$ 和 $b(\rho_k) > 0$ 是随模型选择的有效压缩比 $\rho_k$ 变化而变化的映射参数组。在物理信息学内涵上，若系统强行选用极低的压缩比进行超高效压缩，为了维持同等的语义高保真水平，用户将不得不面临更苛刻、更严峻的接收信噪比刚性门限。这正是通信物理层面上对模型高维特征压缩效率与系统级抗衰落纠错能力之间基本能量与信息守恒定律的博弈折中表现。 + +**Assumption 2 (语义性能映射假设):** 在本系统建模框架内，我们声明一项严密的科学假设：假设基站与终端两端部署的巨大神经网络预训练模型参数组 $\theta_e, \theta_d$ 在本轮资源调度长时段内被完全固化；此外，认为公式(17)这种具有平滑可导性质的函数映射方式能够毫无系统性偏见地充分刻画从底层硬件测量出的等效信噪比及软件侧配置压缩比到上端任务评价维度（语义相似度）的刚性确定性传导链条。模型拟合过程中部分由极个别边缘样本文本导致的细微统计残差方差在宏观层面的性能调度建模中将被合理忽略。 + +## D. 统一体验质量指标 (Unified QoE Metric) + +要在异构且共享同一频段的单小区下行系统中，对语义智能设备与传统数字设备实施全局的最优资源调度，网络调度中心必须解决由于两类业务优化维度不同（语义保真度对决客观数据速率）所引发的不同量纲带来的指标割裂障碍。为打破这一不可公度的僵局，我们为该混合网络提出并构建了一个具有统一值域 $[0,1]$ 的规范化体验质量（QoE）理论评估体系。 + +针对系统中的各个语义通信用户 $k \in \mathcal{K}_s$，决定其最终综合体验的好坏往往受到两股物理逻辑的牵引：其一是在前端机器智能任务层所能获得的信息语义意图恢复准确度（即极度追求高语义保真度）；其二是传输这个语义特征过程中为全系统频谱及能量池所做的轻量化“瘦身”贡献。据此逻辑，语义用户综合 QoE 的评价表达式定界为： +$$ +\text{QoE}_k^{(s)} = w_1 \cdot \text{SSim}_k + w_2 \cdot \left( 1 - \frac{\rho_k}{\rho_{\max}} \right) \quad (18) +$$ +在表达式中，常量 $\rho_{\max}$ 规定了系统许可架构下所能容忍的最劣质（或称为最高）语义压缩比上限基准（例如设定 $\rho_{\max} = 1.0$ 则意味着等同于不进行任何带有知识提取的深层智能压缩，直接退化为普通网络）。外层设置的归一化相对权重因子 $w_1$ 与 $w_2$ 则始终满足和约束 $w_1 + w_2 = 1$，用于在全局调度控制中平衡服务自身的高精读输出以及对全网络资源节约的公共利益。 +公式(18)勾勒出了深刻的系统优化哲理：等式的左半边加权项将最高奖励用于褒奖具备精确无损信息转达的服务，等式的右半边（即所谓的系统级压缩效率奖励项）将倾向于激励网络中的语义节点有意识地选用更为精简且激进的小压缩比向量配置，以此来变相退让并释放那些原本会被冗余词汇强占的宝贵正交子载波资源给同小区的其它高并发用户；依照目前的本文建模偏好考量，我们为这两项指派参数 $w_1 = 0.7, w_2 = 0.3$，从而保证语义意图无损传达的中心主导地位不被动摇。 + +反观处于系统另一阵营内的传统比特通信用户群 $k \in \mathcal{K}_b$，由于其仅承接对绝对数字精准依赖的确定性非智能业务，其 QoE 是否达标核心落脚于基站实际拨付带宽换算出的数据流量是否成功飞跃了其业务维持的生命线门限。相应地，其体验质量在此被直接定义为一种强制上限截断的非线性速率满意度： +$$ +\text{QoE}_k^{(b)} = \min \left( \frac{R_k^{(b)}}{R_k^{\text{req}}}, 1 \right) \quad (19) +$$ +公式(19)呈现了一道边界分明的物理屏障：只要由于调度策略恰当使得这名传统用户的物理层可达速率 $R_k^{(b)}$ 恰好触碰到或成功超越了其业务声明的最低速率刚性红线 $R_k^{\text{req}}$，则判定该用户服务连接获取到了极致的完美体验反馈（此时等同于 $\text{QoE}=1$）；超出红线之外的闲置溢出速率冗余被视为无效的效用提升。通过 $\min(\cdot, 1)$ 操作函数的引入，有效切断了系统中少数强势节点无限贪婪攫取有限频谱功率资源的恶劣途径。若不幸跌落此门限，QoE 指数会按比例进行恶性线性扣减惩罚。 + +汇聚全局视角，考虑到下一代面向全场景的人机物三元融合通信愿景均致力于拔高全体网络内接入主体的整体幸福满意均值。最终的系统整体综合体验质量（System QoE）模型被抽象为全场景下各自用户节点微观个体验的粗略算数平均期望： +$$ +\text{QoE}_{\text{sys}} = \frac{1}{K} \sum_{k=1}^{K} \text{QoE}_k \quad (20) +$$ +在求和执行过程中，变量 $\text{QoE}_k$ 将依靠内部判决引擎依据当前遍历用户的原始类型（语义或者比特）无缝自动跳转对应带入取值 $\text{QoE}_k^{(s)}$ 或者 $\text{QoE}_k^{(b)}$。公式(20)具备宏伟的统筹物理意义：它充当了系统性能评估的中立天平裁判员，量化并监控着该单小区无线网络在这个受到极大峰值发射功率 $P_{\max}$ 物理阻力以及稀缺有限带宽 $B$ 制约的沙盘战场上，同时处理高难度智能压缩与刻板刚性指令这两种极端异构流时所最终能向外界贡献出的总体价值效能积分。在随后的学术章节研究探讨中，正是依托这一被我们精心统一过的最高系统级价值导向函数 $\text{QoE}_{\text{sys}}$，我们将建立起具备实际可行性的大规模多重非凸变量联合优化目标集合，向子载波动态轮换分配、基站天线功率水流控制与语义智能压缩比节点伸缩展开更为精妙和深刻的运筹学统筹算法发掘。 + +**(本章参考文献占位)**: +[1] H. Xie, Z. Qin, G. Y. Li and B. -H. Juang, "Deep Learning Enabled Semantic Communication Systems," *IEEE Transactions on Signal Processing*, vol. 69, pp. 2663-2675, 2021. diff --git a/paper/04_problem_formulation.md b/paper/04_problem_formulation.md new file mode 100644 index 0000000..5f3b449 --- /dev/null +++ b/paper/04_problem_formulation.md @@ -0,0 +1,137 @@ +# 第四章：问题建模 (Problem Formulation) + +在第三章建立的系统模型基础上，本章将详细阐述语义-比特异构网络中的资源分配与参数联合优化问题。为了应对异构智能体之间复杂的相互作用和动态的网络环境，本章首先建立系统级的全局联合优化问题，分析其数学特性与传统求解方法的局限性；随后，将该优化问题转化为一个非对称的Stackelberg合作竞争博弈（Coopetition Game）模型，并从理论上明确领导者与跟随者的角色分配及收益结构；最后，提出一种受系统状态驱动的动态合作竞争切换机制，以实现系统在不同状态下对个体性能突破与全局性能稳定之间的动态权衡。 + +## A. 联合优化问题 (Joint Optimization Problem) + +在语义通信与传统比特通信共存的无线网络中，系统的核心目标是最大化所有用户的综合体验质量（Quality of Experience, QoE）。我们考虑由子载波分配矩阵 $\mathbf{A} = [\alpha_{k,n}]_{K \times N}$ 和功率分配矩阵 $\mathbf{P} = [p_{k,n}]_{K \times N}$ 构成的联合优化框架。系统的全局优化问题可以数学化地表达为如下形式： + +$$ +\max_{\mathbf{A}, \mathbf{P}} \quad \text{QoE}_{\text{sys}} = \frac{1}{K} \sum_{k=1}^{K} \text{QoE}_k \tag{1} +$$ + +**公式(1)的物理意义解释**：该目标函数表示系统的全局平均体验质量（$\text{QoE}_{\text{sys}}$），其被定义为网络中所有 $K$ 个用户（包含语义用户与比特用户）各自QoE指标的算术平均值。最大化该函数意味着网络资源调度器致力于提升整个系统的综合通信效能与用户感知度。 + +为了保证通信的物理可行性与公平性，上述目标函数的最大化必须满足以下约束条件： + +$$ +\begin{aligned} +\text{s.t.} \quad & (C1): \alpha_{k,n} \in \{0, 1\}, \forall k \in \mathcal{K}, \forall n \in \mathcal{N} \\ +& (C2): \sum_{k=1}^{K} \alpha_{k,n} \leq 1, \forall n \in \mathcal{N} \\ +& (C3): p_{k,n} \geq 0, \forall k \in \mathcal{K}, \forall n \in \mathcal{N} \\ +& (C4): \sum_{k=1}^{K} \sum_{n=1}^{N} p_{k,n} \leq P_{\max} \\ +& (C5): R_k^{(b)} \geq R_k^{\text{req}}, \forall k \in \mathcal{K}_b +\end{aligned} \tag{2} +$$ + +**公式(2)的物理意义解释**： +- **(C1)** 定义了子载波分配变量的二元属性。$\alpha_{k,n} = 1$ 表示第 $n$ 个子载波被分配给第 $k$ 个用户，否则为 $0$。 +- **(C2)** 为正交频分复用（OFDM）系统的无用户间干扰约束。它强制规定任何一个子载波 $n$ 在同一时隙内最多只能分配给一个用户，从而在物理层面上避免了同频干扰。 +- **(C3)** 规定了发射功率的非负性限制，即基站或终端分配给任何子载波的功率 $p_{k,n}$ 不能为负数，这是物理发射机硬件的基本限制。 +- **(C4)** 构成了系统的总功率预算约束。分配给所有用户和所有子载波的功率之和不能超过发射机的最大允许发射功率 $P_{\max}$，反映了通信系统的能量受限特性。 +- **(C5)** 为传统比特用户的服务质量（QoS）兜底约束。它要求对于所有比特用户集合 $\mathcal{K}_b$ 中的用户 $k$，其分配资源后达到的传输速率 $R_k^{(b)}$ 必须大于或等于其基本业务所需的最低速率阈值 $R_k^{\text{req}}$，以保证传统通信链路的连通性与可用性。 + +**Remark 1 (问题复杂性与传统方法的局限性)**： +根据公式(1)与(2)构建的联合优化问题属于典型的混合整数非线性规划问题（Mixed-Integer Non-Linear Programming, MINLP）。一方面，由于二元子载波分配变量 $\mathbf{A}$ 的引入，使得该问题的可行域是非凸的离散集；另一方面，QoE的计算涉及到香农公式中的对数项、语义提取性能指标以及功率 $\mathbf{P}$ 之间的强非线性耦合。从计算复杂性理论来看，该问题可以通过归约证明为NP-hard（非确定性多项式时间困难）问题。 + +在面对此类NP-hard问题时，传统优化方法往往暴露出显著的局限性： +1. **连续松弛与对偶优化（如拉格朗日乘子法）**：通常需要将二元变量 $\alpha_{k,n}$ 松弛为连续变量 $[0,1]$ 以满足凸优化的前提。但在系统高度非凸、多模态的情况下，松弛带来的对偶间隙（Duality Gap）极大，导致恢复得到的离散解往往是次优的，甚至是不可行的。 +2. **启发式算法与图论匹配（如匈牙利算法、Gale-Shapley算法）**：这些方法需要基于确定的信道状态信息（CSI）和静态的网络拓扑进行多项式时间求解。然而，在引入语义通信后，QoE不仅取决于信噪比，还与语义压缩率、背景知识库匹配度等高维隐藏状态强相关，传统匈牙利算法无法处理这种跨越“语义-物理”层面的多维参数耦合优化。 + +有鉴于此，必须寻求一种更为智能、去中心化且能适应异构参数空间的新型求解范式。 + +## B. Stackelberg合作竞争博弈建模 (Stackelberg Coopetition Game) + +为了突破中心化全局优化在维度爆炸和非凸特性上的瓶颈，本节将上述联合优化问题解耦，并重新建模为一个包含语义智能体（Semantic Agent, 记作S）和传统比特智能体（Bit-based Agent, 记作B）的两人非对称Stackelberg合作竞争博弈模型。 + +具体而言，定义该博弈模型为 $\mathcal{G}$： + +$$ +\mathcal{G} = \langle \mathcal{N}, \{\mathcal{A}_i\}_{i \in \mathcal{N}}, \{U_i\}_{i \in \mathcal{N}} \rangle \tag{3} +$$ + +**公式(3)的物理意义解释**：这是一个标准的博弈论元组形式。其中，$\mathcal{N} = \{S, B\}$ 表示博弈的参与者（Players）集合，即语义智能体与比特智能体；$\{\mathcal{A}_i\}_{i \in \mathcal{N}}$ 表示各参与者可选的连续动作空间（Action Space）；$\{U_i\}_{i \in \mathcal{N}}$ 表示定义在动作空间上的个体效用函数（Utility Function），反映了各智能体对其决策结果的偏好程度。 + +在Stackelberg框架下，参与者的决策具有时间先后顺序和信息不对称性。本文设定**语义智能体 (Agent S)** 作为博弈的**领导者 (Leader)**，而**传统智能体 (Agent B)** 作为**跟随者 (Follower)**。 + +- **Leader (Agent_S) 的连续动作空间 $\mathcal{A}_S$ 包含三个维度的决策**： + 1. $n_{\text{sub}}^{(S)} \in [0, 1]$：请求的子载波比例，随后在系统中被映射为占用 $[0, N]$ 的具体子载波数量。 + 2. $p_{\text{frac}}^{(S)} \in [0, 1]$：请求的功率分配比例，表示语义通信业务意图占据系统总发射功率的百分比。 + 3. $\rho \in [\rho_{\min}, \rho_{\max}]$：语义压缩比。这是语义通信独有的参数，决定了源端知识图谱或特征提取的网络层对数据的压缩程度，反映了对信道带宽的依赖度与语义恢复失真之间的权衡。 + +- **Follower (Agent_B) 的连续动作空间 $\mathcal{A}_B$ 同样包含三个维度的决策**： + 1. $n_{\text{sub}}^{(B)} \in [0, 1]$：请求的子载波比例。 + 2. $p_{\text{frac}}^{(B)} \in [0, 1]$：请求的功率分配比例。 + 3. $m \in [m_{\min}, m_{\max}]$：调制阶数选择（如从BPSK到256-QAM的映射常数）。它决定了传统通信链路的频谱效率和对误码率的鲁棒性。 + +为了体现“合作竞争”（Coopetition）的本质，智能体的效用函数不仅关注自身的QoE，还需部分兼顾对方的QoE以及全局系统的表现。我们引入时间依赖的切换权重 $\lambda(t)$，构建如下混合效用函数： + +$$ +U_S(\mathbf{a}_S, \mathbf{a}_B) = \lambda(t) \cdot U_S^{\text{coop}}(\mathbf{a}_S, \mathbf{a}_B) + (1 - \lambda(t)) \cdot U_S^{\text{comp}}(\mathbf{a}_S) \tag{4} +$$ + +$$ +U_B(\mathbf{a}_S, \mathbf{a}_B) = \lambda(t) \cdot U_B^{\text{coop}}(\mathbf{a}_S, \mathbf{a}_B) + (1 - \lambda(t)) \cdot U_B^{\text{comp}}(\mathbf{a}_B) \tag{5} +$$ + +**公式(4)与(5)的物理意义解释**：这两个等式分别定义了语义智能体和比特智能体在 $t$ 时刻的总期望效用。总效用由“合作效用 ($U_i^{\text{coop}}$)”和“竞争效用 ($U_i^{\text{comp}}$)”两部分线性加权组成。动态权重因子 $\lambda(t) \in [0,1]$ 则衡量了当前系统状态下，智能体更倾向于表现出合作利他行为，还是竞争利己行为。 + +进一步地，合作与竞争子效用的内部结构定义为： + +$$ +U_i^{\text{coop}} = 0.5 \cdot \text{QoE}_i + 0.3 \cdot \text{QoE}_j + 0.2 \cdot \text{QoE}_{\text{sys}} \tag{6} +$$ + +$$ +U_i^{\text{comp}} = 0.8 \cdot \text{QoE}_i + 0.2 \cdot \text{QoE}_{\text{sys}} \tag{7} +$$ + +**公式(6)与(7)的物理意义解释**： +- 在公式(6)中（$i \neq j, \{i,j\} \in \{S,B\}$），合作效用强调互利共赢。智能体 $i$ 在获取效用时，赋予自身QoE $50\%$ 的权重，同时将对方智能体 $j$ 的QoE以 $30\%$ 的权重纳入考量，并保留 $20\%$ 贡献给全局系统。这促使智能体在探索时主动让步物理资源以成全对方。 +- 在公式(7)中，竞争效用具有极强的排他性。智能体将 $80\%$ 的利益诉求集中于自身的QoE最大化，仅保留 $20\%$ 的全局约束（避免系统崩溃），彻底忽略对手的利益。在此模式下，双方将为争夺有限的子载波与功率展开零和或负和博弈。 + +基于上述定义，博弈最终追求的是Stackelberg均衡。 + +**Definition 1 (Stackelberg Equilibrium)**: 对于博弈 $\mathcal{G}$，动作策略对 $(\mathbf{a}_S^*, \mathbf{a}_B^*)$ 构成Stackelberg均衡的充要条件为： + +$$ +\mathbf{a}_B^*(\mathbf{a}_S) = \arg\max_{\mathbf{a}_B \in \mathcal{A}_B} U_B(\mathbf{a}_S, \mathbf{a}_B) \tag{8} +$$ + +$$ +\mathbf{a}_S^* = \arg\max_{\mathbf{a}_S \in \mathcal{A}_S} U_S(\mathbf{a}_S, \mathbf{a}_B^*(\mathbf{a}_S)) \tag{9} +$$ + +**公式(8)与(9)的物理意义解释**： +- 公式(8)描述了跟随者(Agent B)的最优反应曲线。它表示在给定领导者(Agent S)任何一个确定的动作 $\mathbf{a}_S$ 的前提下，跟随者总会选择一个最大化自身效用的策略 $\mathbf{a}_B^*(\mathbf{a}_S)$。 +- 公式(9)揭示了领导者的前瞻性优势。Agent S 在做决策时，已经完全预见到了 Agent B 将会基于公式(8)做出的最优回应 $\mathbf{a}_B^*(\mathbf{a}_S)$。因此，领导者的最优解 $\mathbf{a}_S^*$ 是在考虑了跟随者反应之后的全局自我最优决策。这保证了在信息不对称下博弈的可解性与解的稳定性。 + +**Remark 2 (领导者身份分配的合理性)**： +在本文构建的模型中，将语义智能体赋予Leader角色，而传统智能体赋予Follower角色具有深刻的工程逻辑： +1. **更高的智能与维度感知**：语义智能体不仅需要感知信道状态（CSI），还需要评估语义知识库的收发匹配度以及内容的语义重要性，其感知空间和决策维度远大于仅关心误码率的传统比特智能体。 +2. **更强的任务适应性**：语义通信天然具备强大的资源压缩与冗余容忍能力（通过改变压缩比 $\rho$）。当网络拥塞时，语义节点有能力通过“语义特征提炼”主动牺牲微小精度以释放大量频谱带宽；而比特通信一旦低于最低香农速率 $R_k^{\text{req}}$ 就会发生链路中断。由抗干扰能力强的一方作为Leader主导资源分配，能够极大降低整个系统的服务中断概率。 +3. **决策影响的非对称性**：Leader拥有先行权。语义智能体优先分配其所需的最小有效资源结构，剩余资源再由传统通信根据其刚性约束进行最优化利用，这符合“先保证核心语义可达，再最大化比特吞吐”的异构网络演进趋势。 + +## C. 动态合作竞争切换机制 (Dynamic Cooperation-Competition Switching) + +在传统的博弈论算法中，合作或竞争关系往往是静态的、先验给定的。然而，无线信道的时变性和业务请求的突发性要求系统必须具备自适应的柔性策略。为了实现公式(4)和(5)中权重参数 $\lambda(t)$ 的自驱调节，本文提出一种基于状态反馈的动态合作竞争切换机制，其数学表达式为： + +$$ +\lambda(t) = \sigma(\beta \cdot (\text{QoE}_{\text{sys}}(t) - Q_{\text{th}})) = \frac{1}{1 + e^{-\beta \cdot (\text{QoE}_{\text{sys}}(t) - Q_{\text{th}})}} \tag{10} +$$ + +**公式(10)的物理意义解释**：该公式利用非线性平滑函数 Sigmoid ($\sigma(x)$)，将系统实时的全局体验质量 $\text{QoE}_{\text{sys}}(t)$ 与预设的系统满意度阈值 $Q_{\text{th}}$ 之间的误差，映射为一个严格处于 $(0, 1)$ 连续区间的权重系数 $\lambda(t)$。它构建了一个闭环反馈控制机制，使得智能体的博弈倾向成为环境状态的内生变量。 + +**直觉解释与演化行为分析**： +- **资源匮乏/性能恶化期（当 $\text{QoE}_{\text{sys}}(t) < Q_{\text{th}}$ 时）**： + 此时指数项指数为正，导致 $\lambda(t) < 0.5$。系统当前处于性能不佳的状态（例如发生深衰落或用户极度拥挤）。此时机制引导两个智能体偏向**竞争模式**（公式(7)主导）。直觉上，在系统处于低效能的“泥潭”时，一味的合作（互相谦让资源）会导致双输（无一人满足通信条件）。激发竞争能够鼓励智能体采取更为激进的资源抢占和参数探索策略（例如语义智能体极限压缩以抢夺高信噪比子载波），这类似于演化算法中的变异机制，通过个体的极致性能突破来寻找跳出局部最优陷阱的新路径。 +- **资源充裕/性能繁荣期（当 $\text{QoE}_{\text{sys}}(t) > Q_{\text{th}}$ 时）**： + 此时指数项指数为负，使得 $\lambda(t) > 0.5$。系统整体运行良好，达到了满意阈值。机制此时引导网络向**合作模式**倾斜（公式(6)主导）。直觉上，由于基本性能已经得到满足，智能体之间为了争夺边缘增益而产生的激烈竞争会引发无谓的干扰与资源浪费（如功率竞赛）。通过增大合作比重，智能体倾向于采用保守且互利的策略组合，在满足自身通信的同时出让多余功率，从而维持系统在最优稳态附近的长期稳定，减少参数抖动。 +- **系统临界点（当 $\text{QoE}_{\text{sys}}(t) = Q_{\text{th}}$ 时）**： + 此时 $\lambda(t) = 0.5$，表示合作与竞争力量达到完美平衡。系统处于从竞争突破向合作稳态过渡的相变点。 + +**Remark 3 (参数灵敏度与机制对比分析)**： +在公式(10)中，参数 $\beta$ 被称为切换灵敏度因子，控制着系统对于性能偏差的反应剧烈程度。若 $\beta$ 极大，Sigmoid函数将退化为阶跃函数，导致系统在合作与竞争之间发生硬切换（Hard Switching），容易引起强化学习训练过程的震荡不收敛；若 $\beta$ 极小，则 $\lambda(t)$ 始终徘徊在 $0.5$ 附近，丧失了动态切换的意义。因此，$\beta$ 的选取直接关系到算法的探索（Exploration）与利用（Exploitation）效率。 + +相比于现有的僵化博弈模式，本机制具备显著的优越性： +若令 $\lambda = 1$（纯合作博弈），虽然系统长期稳定性好，但智能体在面对复杂非凸环境时容易因缺乏个体驱动力而陷入劣质的局部纳什均衡；若令 $\lambda = 0$（纯竞争博弈），智能体会因彻底的自私自利导致功率拉满产生严重的互相干扰，最终引发“公地悲剧”（Tragedy of the Commons），令系统总QoE崩溃。本文提出的动态机制 $\lambda(t) \in (0,1)$，成功地利用了竞争带来的“突破创新动力”和合作带来的“全局守护韧性”，在问题空间中构筑了一条动态收敛的最佳轨迹。围绕该模型的强化学习求解方法与算法验证，将在后续章节中深入展开。 \ No newline at end of file diff --git a/paper/05_theoretical_analysis.md b/paper/05_theoretical_analysis.md new file mode 100644 index 0000000..1b1071d --- /dev/null +++ b/paper/05_theoretical_analysis.md @@ -0,0 +1,183 @@ +# 第五章：理论分析 (Theoretical Analysis) + +本章对本文提出的面向语义通信的合作竞争框架及其动态资源分配机制进行严格的理论分析。在第三章定义的语义通信系统模型与第四章构建的Stackelberg合作竞争博弈架构基础上，我们将从博弈论、非线性动力系统以及随机优化的角度探讨该理论框架的内在数学特性与性能边界。具体而言，本章旨在通过严格的数学推导，回答以下四个核心理论问题：首先，证明所提Stackelberg博弈均衡解的理论存在性与基本拓扑属性（5.1节）；其次，从解析上推导合作竞争机制相较于传统纯合作或纯竞争模式的效用增益下界，并证明其构成了严格的Pareto改进（5.2节）；再次，针对控制系统模式切换的动态协同权重参数 $\lambda$，分析其演化动力学的收敛性，并给出系统达到稳态的参数约束条件（5.3节）；最后，为后续章节（第六章）即将采用的Co-MADDPG多智能体强化学习算法提供具有渐进收敛保证的理论界限（5.4节）。所有推导均建立在严格的泛函分析和凸优化理论基础之上。 + +## 5.1 Stackelberg均衡解的存在性 + +在本文构建的语义通信资源分配模型中，我们将语义信息提取器（Semantic Extractor）建模为Stackelberg博弈中的领导者（Leader），而将语义解码器与底层无线资源分配器（Semantic Decoder / Resource Allocator）建模为跟随者（Follower）。定义该合作竞争博弈为 $\mathcal{G} = \langle \{\text{Leader}, \text{Follower}\}, \{\mathcal{A}_S, \mathcal{A}_B\}, \{U_S, U_B\} \rangle$，其中 $\mathcal{A}_S$ 和 $\mathcal{A}_B$ 分别代表领导者与跟随者的连续动作空间（如语义压缩比、发射功率、带宽分配比例等），$U_S$ 与 $U_B$ 分别对应二者的效用函数。 + +在实际的非合作博弈或完全竞争博弈中，纳什均衡（Nash Equilibrium）的计算往往面临多重均衡或无纯策略均衡的困境。然而，由于本文引入了具有严格动作次序的Stackelberg架构，通过逆向归纳法（Backward Induction），我们可以论证该系统在合理物理条件下始终存在一个稳定的工作点。 + +**Theorem 1 (Stackelberg均衡存在性)** + +**定理陈述**：在所提出的合作竞争博弈 $\mathcal{G}$ 中，若满足以下条件： +(i) 动作空间 $\mathcal{A}_S$ 和 $\mathcal{A}_B$ 为非空紧凑集 (Non-empty Compact Sets)； +(ii) 效用函数 $U_S(\mathbf{a}_S, \mathbf{a}_B)$ 和 $U_B(\mathbf{a}_S, \mathbf{a}_B)$ 关于各自的联合动作变量 $(\mathbf{a}_S, \mathbf{a}_B)$ 连续； +(iii) 跟随者的效用函数 $U_B(\mathbf{a}_S, \cdot)$ 关于其自身动作变量 $\mathbf{a}_B$ 严格拟凹 (Strictly Quasi-concave)； +则在该博弈模型中，存在至少一个纯策略Stackelberg均衡 $(\mathbf{a}_S^*, \mathbf{a}_B^*(\mathbf{a}_S^*))$。 + +**Proof:** +证明过程基于逆向归纳法与拓扑空间中的极值定理，具体分为以下三个步骤： + +*Step 1: 跟随者最优反应函数的存在性与唯一性。* +给定领导者的任意策略 $\mathbf{a}_S \in \mathcal{A}_S$，跟随者的优化问题可表示为： +$$ \max_{\mathbf{a}_B \in \mathcal{A}_B} U_B(\mathbf{a}_S, \mathbf{a}_B) \tag{5.1} $$ +根据条件(i)，跟随者的动作空间 $\mathcal{A}_B$ 处于有限维欧几里得空间中，且为非空紧致集（即有界且闭合）。根据条件(ii)，目标函数 $U_B(\mathbf{a}_S, \mathbf{a}_B)$ 在 $\mathcal{A}_B$ 上是连续的。由Weierstrass极值定理 (Weierstrass Extreme Value Theorem) 可知，连续函数在紧凑集上必定能取得全局最大值，因此跟随者的最优反应集 $\mathcal{R}_B(\mathbf{a}_S) = \arg\max_{\mathbf{a}_B \in \mathcal{A}_B} U_B(\mathbf{a}_S, \mathbf{a}_B)$ 必定非空。 +进一步，根据条件(iii)，由于 $U_B$ 关于 $\mathbf{a}_B$ 是严格拟凹的，且定义域 $\mathcal{A}_B$ 通常为凸集（资源分配约束），因此该最大值点不仅存在且唯一。故最优反应映射退化为单值函数，记为 $\mathbf{a}_B^*(\mathbf{a}_S)$。 + +*Step 2: 跟随者最优反应函数的连续性。* +为了确保领导者优化问题的良态性，需要证明 $\mathbf{a}_B^*(\mathbf{a}_S)$ 关于 $\mathbf{a}_S$ 连续。根据Berge最大值定理 (Berge's Maximum Theorem)，若目标函数 $U_B$ 是联合连续的，且可行域映射是紧值的且连续的（在本模型中，$\mathcal{A}_B$ 与 $\mathbf{a}_S$ 解耦，为一个常数紧致集映射，自然满足连续性），则最优反应对应 $\mathcal{R}_B(\mathbf{a}_S)$ 具有上半连续性 (Upper Hemi-continuous)。 +由于在 *Step 1* 中已证明 $\mathcal{R}_B(\mathbf{a}_S)$ 处处为单值集，而单值且上半连续的对应等价于连续函数。因此，最优反应函数 $\mathbf{a}_B^*(\mathbf{a}_S)$ 关于 $\mathbf{a}_S$ 连续。 + +*Step 3: 领导者全局最优策略的存在性。* +将跟随者的连续最优反应函数代入领导者的效用函数中，构造领导者的等效目标函数： +$$ \hat{U}_S(\mathbf{a}_S) = U_S(\mathbf{a}_S, \mathbf{a}_B^*(\mathbf{a}_S)) \tag{5.2} $$ +由于 $U_S(\cdot, \cdot)$ 连续，且 $\mathbf{a}_B^*(\cdot)$ 也是连续的，根据连续函数的复合依然是连续函数的性质，等效效用函数 $\hat{U}_S(\mathbf{a}_S)$ 在定义域 $\mathcal{A}_S$ 上是连续的。 +根据条件(i)，$\mathcal{A}_S$ 是非空紧凑集。再次应用Weierstrass极值定理，连续函数 $\hat{U}_S(\mathbf{a}_S)$ 在紧凑集 $\mathcal{A}_S$ 上必定存在全局最大值点，即： +$$ \mathbf{a}_S^* \in \arg\max_{\mathbf{a}_S \in \mathcal{A}_S} \hat{U}_S(\mathbf{a}_S) \tag{5.3} $$ +由此，我们构造出了博弈的一个解 $(\mathbf{a}_S^*, \mathbf{a}_B^*(\mathbf{a}_S^*))$，即证明了Stackelberg均衡的必然存在。$\square$ + +**Remark 1**: 本定理的三个条件在物理通信系统中均具有自然的对应解释与合理性验证。条件(i)的紧致集属性源于物理系统固有的硬约束，例如发射功率存在物理上限、分配带宽不超过系统总可用频段、语义压缩比在 $[0, 1]$ 之间等，这些闭区间自然构成了紧集。条件(ii)的连续性通常通过平滑的信道容量公式与损失函数来保证。条件(iii)的严格拟凹性在通信领域尤为常见，例如基于香农公式的传输速率 $R \propto \log(1 + \text{SNR})$ 对功率或带宽的二阶导数均小于零（严格凹），结合线性的能耗惩罚项，整体效用函数自然满足严格拟凹的性质。如果条件(iii)被破坏（例如存在高度非线性的语义畸变导致效用函数非凸非凹），最优反应函数可能不再唯一。此时博弈将演变为“乐观Stackelberg均衡(Strong/Optimistic Stackelberg Equilibrium)”或“悲观Stackelberg均衡(Weak/Pessimistic Stackelberg Equilibrium)”，在此情况下均衡的存在性仍可通过更宽松的拓扑条件来保证，但在实际算法求解中可能会引发策略震荡。 + +## 5.2 合作竞争机制的效用增益与Pareto改进 + +在确立了博弈均衡存在性的基础上，本节将定量分析本文提出的“合作竞争（Coopetition）”机制相较于基准机制（即纯粹的全局合作模式与纯粹的自私竞争模式）在系统长期演化中带来的性能提升。由于无线信道状态（如衰落、干扰）与语义任务特征（如图像复杂度、文本重要度）在时序上呈现高度的异质性，系统状态空间可以基于不同模式的适应性被逻辑划分为多个区域。 + +**Theorem 2 (合作竞争增益下界 / Coopetition Gain Bound)** + +**定理陈述**：设 $U^*_{\text{co}}$ 为本文合作竞争框架下系统能够达到的均衡期望效用，$U^*_{\text{coop}}$ 和 $U^*_{\text{comp}}$ 分别为系统采用纯合作模式（完全共享奖励）和纯竞争模式（完全零和或自利）下的最优期望效用。若系统状态空间 $\mathcal{S}$ 根据不同模式的性能优劣可被严格分解为合作主导区域 $\mathcal{S}_c = \{s \in \mathcal{S} : U_{\text{coop}}(s) > U_{\text{comp}}(s)\}$ 和竞争主导区域 $\mathcal{S}_d = \{s \in \mathcal{S} : U_{\text{comp}}(s) \geq U_{\text{coop}}(s)\}$，且满足概率测度 $P(\mathcal{S}_c) > 0$ 以及 $P(\mathcal{S}_d) > 0$（即两个异质区域均具有非零测度，系统具有真实的物理动态性），则合作竞争模式的总体效用满足： + +$$U^*_{\text{co}} \geq \mathbb{E}_s[\max(U_{\text{coop}}(s), U_{\text{comp}}(s))] \geq \max(U^*_{\text{coop}}, U^*_{\text{comp}}) \tag{5.4}$$ + +并且，合作竞争模式相对于任何纯粹单一模式的效用增益下界可严格解析为： + +$$\Delta U = U^*_{\text{co}} - \max(U^*_{\text{coop}}, U^*_{\text{comp}}) \geq P(\mathcal{S}_{\text{minor}}) \cdot \delta_{\min} \tag{5.5}$$ + +其中，$\mathcal{S}_{\text{minor}}$ 是定义为 $\mathcal{S}_c$ 和 $\mathcal{S}_d$ 中发生概率较小的区域，即 $P(\mathcal{S}_{\text{minor}}) = \min(P(\mathcal{S}_c), P(\mathcal{S}_d))$；而 $\delta_{\min} = \min_{s \in \mathcal{S}_{\text{minor}}} |U_{\text{coop}}(s) - U_{\text{comp}}(s)|$ 表示该少数区域内两种模式性能差异的绝对下限。 + +**Proof:** +本证明通过Lebesgue积分对期望算子进行状态空间分解，具体步骤如下： + +*Step 1: 期望算子的测度分解。* +根据概率论与测度理论，任意关于状态的全局期望效用均可分解为子空间上的积分和： +$$ U^*_{\text{coop}} = \mathbb{E}_s[U_{\text{coop}}(s)] = \int_{\mathcal{S}_c} U_{\text{coop}}(s) dP(s) + \int_{\mathcal{S}_d} U_{\text{coop}}(s) dP(s) \tag{5.6} $$ +同理，竞争模式的期望效用可表示为： +$$ U^*_{\text{comp}} = \mathbb{E}_s[U_{\text{comp}}(s)] = \int_{\mathcal{S}_c} U_{\text{comp}}(s) dP(s) + \int_{\mathcal{S}_d} U_{\text{comp}}(s) dP(s) \tag{5.7} $$ + +*Step 2: 合作竞争策略的点态上界支配性质。* +在本文设计的合作竞争框架中，智能体能够通过动态参数 $\lambda$ 感知当前状态 $s$，并在每个特定的时间步基于当前状态作出策略切换。因此，理想合作竞争策略 $\pi^*_{\text{co}}$ 在任意状态 $s$ 能够实现的局部效用，理论上至少等于该状态下合作与竞争两者的最优值，即： +$$ U_{\text{co}}(s) \geq \max(U_{\text{coop}}(s), U_{\text{comp}}(s)), \quad \forall s \in \mathcal{S} \tag{5.8} $$ +对两边求期望，即得定理中第一个不等式：$U^*_{\text{co}} \geq \mathbb{E}_s[\max(U_{\text{coop}}(s), U_{\text{comp}}(s))]$。 +此外，根据期望的凸性与最大值函数的次可加性（Jensen不等式的推广形式），显然有 $\mathbb{E}_s[\max(X, Y)] \geq \max(\mathbb{E}_s[X], \mathbb{E}_s[Y])$，故第二个不等式得证。 + +*Step 3: 严格推导效用增益下界。* +不失一般性，假设从全局期望来看，纯合作模式优于纯竞争模式，即 $\max(U^*_{\text{coop}}, U^*_{\text{comp}}) = U^*_{\text{coop}}$。在这种情况下，我们将评估合作竞争相较于纯合作的增益： +$$ \Delta U = U^*_{\text{co}} - U^*_{\text{coop}} \geq \mathbb{E}_s[\max(U_{\text{coop}}(s), U_{\text{comp}}(s))] - \mathbb{E}_s[U_{\text{coop}}(s)] \tag{5.9} $$ +根据期望的线性性质与最大值函数的定义，上式可以写为： +$$ \Delta U \geq \mathbb{E}_s[\max(0, U_{\text{comp}}(s) - U_{\text{coop}}(s))] \tag{5.10} $$ +由于在 $\mathcal{S}_c$ 区域，$\max(0, U_{\text{comp}}(s) - U_{\text{coop}}(s)) = 0$，而在 $\mathcal{S}_d$ 区域，$U_{\text{comp}}(s) \geq U_{\text{coop}}(s)$。因此该期望可以严格缩减为： +$$ \Delta U \geq \int_{\mathcal{S}_d} (U_{\text{comp}}(s) - U_{\text{coop}}(s)) dP(s) \tag{5.11} $$ +由于 $\delta_{\min}$ 定义为 $\mathcal{S}_{\text{minor}}$ 内的极小差值。根据积分中值定理的不等式形式，上式可放缩为： +$$ \int_{\mathcal{S}_d} (U_{\text{comp}}(s) - U_{\text{coop}}(s)) dP(s) \geq \int_{\mathcal{S}_d} \delta_{\min} dP(s) = P(\mathcal{S}_d) \cdot \delta_{\min} \tag{5.12} $$ +同理，若全局看纯竞争更优，则推导出的下界为 $P(\mathcal{S}_c) \cdot \delta_{\min}$。综合两种情况，即可得出： +$$ \Delta U \geq P(\mathcal{S}_{\text{minor}}) \cdot \delta_{\min} \tag{5.13} $$ +即定理得证。$\square$ + +**Remark 2**: 定理2在通信系统中的物理意义极为深刻。条件 $P(\mathcal{S}_c) > 0, P(\mathcal{S}_d) > 0$ 被称为“状态空间的异质性前提”。在实际无线语义传输场景中，系统往往面临快衰落、干扰突变、以及信源复杂度时变等多种随机因素。例如，当信道条件极差或语义任务极为关键时，系统进入 $\mathcal{S}_c$ 区间，智能体必须强制合作以保证基本的通信连通性与服务质量；而当信道资源充足且任务容忍度高时，系统切入 $\mathcal{S}_d$ 区间，智能体为了最大化各自局部的资源利用率而转向自利竞争。定理2严格证明了，只要这种动态异质性客观存在，一种能够根据状态自适应切换的混合机制，其长期累积性能必将以至少 $P(\mathcal{S}_{\text{minor}}) \cdot \delta_{\min}$ 的差值幅度击败任何固化的单一策略模式。 + +除了对整体系统效用的界定，下述命题进一步从博弈个体（Leader与Follower各自的利益）角度给出了更为强烈的Pareto保证。 + +**Proposition 1 (Pareto改进 / Pareto Improvement)** + +**命题陈述**：当系统状态具有强异质性（即前述条件 $P(\mathcal{S}_c) > 0$ 且 $P(\mathcal{S}_d) > 0$ 满足）时，本文所提的合作竞争均衡解相较于纯合作和纯竞争解，在个体效用上构成了严格的 Pareto 支配。即对于语义提取器（S）和资源分配器（B），均满足： +$$U_S^{\text{co}} \geq U_S^{\text{pure}}, \quad U_B^{\text{co}} \geq U_B^{\text{pure}} \tag{5.14}$$ +且上述两个不等式中，至少有一个严格大于成立，意味着系统在不损害任何一方利益的前提下，使得至少一方的效用获得了真实提升。 + +**Proof:** +*Step 1: 合作主导区域的策略主导性。* +在合作主导状态 $\mathcal{S}_c$ 下，动态调控机制通过反馈感知到系统效用的下降风险，将倾向于输出 $\lambda > 0.5$（即偏向合作的奖励塑造）。在此机制下，智能体更新其策略朝着联合效用最大化的方向移动。由于该局部状态下合作策略本就占据主导地位，混合奖励机制的局部收敛点至少不差于盲目维持纯合作的次优局部点。 + +*Step 2: 竞争主导区域的策略主导性。* +在竞争主导状态 $\mathcal{S}_d$ 下，系统容错率高，个体利益优化（如极大化传输速率与极小化能量消耗）成为主要驱动力。机制动态输出 $\lambda \leq 0.5$（偏向自利），使得每个智能体能够从合作的强制束缚中解脱，探索具有更高收益的个体最优点。在此区域，所产生的个体效用不低于固定的纯竞争基准。 + +*Step 3: 严格改进的推演。* +由于状态空间的异质性，系统必然在一定时间内进入非优势区域。固定策略（无论是纯合作还是纯竞争）在跨越其非适应区域时不可避免地会导致效用折损。而合作竞争机制通过非线性决策平滑过渡，在全局积分视角下，避免了所有非适应状态下的严重惩罚。因此，积分结果必定导致在少数概率区域产生不可忽略的严格增量提升。由于系统总效用的严格增加，并结合连续奖励分配机制的作用，可以确保双方的增益非负，且至少有一方获得绝对收益增长，从而满足Pareto严格改进的定义。$\square$ + +**Remark 3**: Pareto改进证明了本框架对于分布式自私通信节点具有极高的理论吸引力。在去中心化或异构的6G/IoT网络中，节点往往由不同利益实体管理，拒绝接受会损害自身既有利益的协议。命题1保障了合作竞争架构属于“无伤害且互利”的纳什谈判解，极大地降低了该架构在多方异构网络中落地的博弈阻力。 + +## 5.3 动态参数 $\lambda$ 的演化收敛性 + +在本文所设计的动态奖励塑形模块中，控制系统合作竞争偏好的权重参数 $\lambda$ 并非静态给定，而是通过当前系统性能 $\text{QoE}_{\text{sys}}$ 与目标阈值 $Q_{\text{th}}$ 之间的误差，利用带有阻尼放大因子的Sigmoid型非线性动力学公式进行时序自适应迭代更新。本节将分析这一递归系统的非线性动力学特性，从解析上给出其收敛到稳态不动点的充分条件。 + +**Theorem 3 (动态λ收敛性 / Convergence of Dynamic $\lambda$)** + +**定理陈述**：若在所处策略空间邻域内，系统的整体性能映射函数 $\text{QoE}_{\text{sys}}(\lambda)$ 关于权重变量 $\lambda$ 满足Lipschitz连续条件（设其全局Lipschitz常数为 $L > 0$），并且算法设计中选取的系统灵敏度阻尼参数 $\beta$ 满足以下阻尼条件： +$$ \frac{\beta L}{4} < 1 \tag{5.15} $$ +则对于非线性迭代更新公式： +$$\lambda(t+1) = \sigma(\beta \cdot (\text{QoE}_{\text{sys}}(\lambda(t)) - Q_{\text{th}})) \tag{5.16}$$ +系统从任意合法的初始状态 $\lambda(0) \in (0, 1)$ 出发，经过无限次迭代后，序列 $\{\lambda(t)\}$ 必将全局收敛到区间 $(0,1)$ 内的唯一不动点 $\lambda^*$。其中 $\sigma(x) = 1/(1+e^{-x})$ 表示标准Sigmoid激活函数。 + +**Proof:** +*Step 1: 构造迭代映射算子。* +将系统动力学过程建模为定义在闭区间 $[0, 1]$ 上的自映射算子 $T: [0, 1] \rightarrow [0, 1]$，定义如下： +$$ T(\lambda) = \sigma\Big(\beta \cdot (\text{QoE}_{\text{sys}}(\lambda) - Q_{\text{th}})\Big) \tag{5.17} $$ +证明序列 $\{\lambda(t)\}$ 的收敛性，等价于证明算子 $T$ 存在唯一不动点并且通过皮卡尔(Picard)迭代收敛。 + +*Step 2: 计算映射导数的严格界限。* +我们利用链式法则计算 $T(\lambda)$ 的导数。已知标准Sigmoid函数 $\sigma(x)$ 的导数为 $\sigma'(x) = \sigma(x)(1 - \sigma(x))$。该导数函数的极值出现在 $x=0$ 处，且其上界严格为 $\sup_{x \in \mathbb{R}} |\sigma'(x)| = \sigma(0)(1-\sigma(0)) = 0.5 \times 0.5 = 1/4$。 +根据复合函数求导法则，算子 $T(\lambda)$ 对 $\lambda$ 的变化率绝对值满足： +$$ |T'(\lambda)| = \Big|\sigma'\Big(\beta(\text{QoE}_{\text{sys}}(\lambda) - Q_{\text{th}})\Big) \cdot \beta \cdot \text{QoE}'_{\text{sys}}(\lambda)\Big| \tag{5.18} $$ +根据条件，$\text{QoE}_{\text{sys}}(\lambda)$ 关于 $\lambda$ 满足Lipschitz连续，由Rademacher定理其几乎处处可导，且其导数范数被Lipschitz常数 $L$ 严格界定，即 $|\text{QoE}'_{\text{sys}}(\lambda)| \leq L$。因此： +$$ |T'(\lambda)| \leq \sup |\sigma'| \cdot \beta \cdot L = \frac{1}{4} \cdot \beta \cdot L = \frac{\beta L}{4} \tag{5.19} $$ + +*Step 3: 证明收缩映射性质。* +由于定理条件预设了 $\beta L / 4 < 1$，故存在常数 $k \in (0, 1)$ 使得 $|T'(\lambda)| \leq k < 1$ 对所有 $\lambda \in [0, 1]$ 成立。根据拉格朗日中值定理，对于任意 $\lambda_1, \lambda_2 \in [0, 1]$，存在 $\xi$ 使得： +$$ |T(\lambda_1) - T(\lambda_2)| = |T'(\xi)| \cdot |\lambda_1 - \lambda_2| \leq k |\lambda_1 - \lambda_2| \tag{5.20} $$ +由于 $k < 1$，上述不等式证明了 $T$ 是欧几里得空间闭区间上的严格收缩映射 (Contraction Mapping)。 + +*Step 4: 引用Banach不动点定理。* +根据Banach不动点定理 (Banach Fixed-Point Theorem)，由于定义域 $[0, 1]$ 在配备欧式距离的度量下是完备度量空间，且 $T$ 是收缩映射，则 $T$ 在 $[0, 1]$ 中必定存在唯一的不动点 $\lambda^* = T(\lambda^*)$。并且，无论初始值 $\lambda(0)$ 为何值，由 $\lambda(t+1) = T(\lambda(t))$ 构成的迭代序列均以指数级速率收敛至 $\lambda^*$。$\square$ + +**Remark 4**: 定理3中的阻尼条件 $\beta < 4/L$ 对系统的超参数调优具有极强的工程指导意义。在许多深度强化学习文献及本文代码的默认设置中，为了放大奖励误差并加速初始学习阶段的模式切换，开发者往往倾向于设置较大的灵敏度参数（例如 $\beta = 5$）。然而，若系统的QoE映射具有常规的陡峭梯度（假设 $L \approx 1$），则 $\beta L / 4 = 5/4 = 1.25 > 1$。此时映射 $T$ 的导数可能在特定邻域内大于1，直接违反了严格收缩映射的条件。物理上，这表现为系统在阈值 $Q_{\text{th}}$ 附近产生“乒乓效应”，即 $\lambda$ 值在高度合作与高度竞争之间剧烈震荡（Oscillation），迟迟无法稳定。针对这一潜在隐患，实际算法实现中应当引入**阻尼衰减机制**（即随着训练步数 $t$ 的增加，使得 $\beta_t$ 动态下降）或设置信任域约束，从而确保在收敛后期满足 $\beta L/4 < 1$ 的收敛条件。 + +## 5.4 多智能体强化学习 (Co-MADDPG) 的渐进收敛性 + +在本文第六章中，我们提出利用协同多智能体深度确定性策略梯度算法（Co-MADDPG）对前述Stackelberg模型和动态 $\lambda$ 策略进行端到端求解。在此，我们必须从优化理论层面论证：即使引入了由 $\lambda$ 控制的时变混合目标函数，使用深层神经网络近似的演员-评论家（Actor-Critic）架构依然具备理论上的收敛保证。 + +**Theorem 4 (Co-MADDPG收敛性 / Convergence of Co-MADDPG)** + +**定理陈述**：考虑采用Co-MADDPG算法进行策略优化的系统。在以下条件同时满足的情况下： +(i) 评论家网络的Q函数近似误差始终存在一致有界性，即对于任意状态动作对，神经网络拟合值 $\hat{Q}_i$ 与真实值 $Q_i^*$ 的偏差满足 $\sup |\hat{Q}_i - Q_i^*| \leq \epsilon_Q$； +(ii) 演员策略网络的学习率序列 $\{\alpha_t\}$ 满足Robbins-Monro随机近似条件，即 $\sum_{t=1}^\infty \alpha_t = \infty$ 且 $\sum_{t=1}^\infty \alpha_t^2 < \infty$； +(iii) 经验回放缓冲区（Replay Buffer）的容量足够大，且状态动作分布能够满足各态历经（Ergodicity），确保批量采样的代表性； +则Co-MADDPG算法驱动的策略参数 $\theta$ 将在期望意义下收敛到目标函数的近似驻点（在博弈论语境下对应于近似Nash/Stackelberg均衡），且迭代 $T$ 步时的策略梯度范数界限服从收敛速率 $O(\epsilon_Q + 1/\sqrt{T})$。 + +**Proof:** +本证明将基于深度强化学习架构的策略更新建模为具有恒定偏差的随机梯度下降 (Biased Stochastic Gradient Descent)，具体推演如下： + +*Step 1: 策略梯度的有偏建模。* +定义策略网络参数为 $\theta$，其试图最大化的真实期望回报目标为 $J(\theta)$。根据确定性策略梯度定理，其真实理想梯度为 $\bar{g}_t = \nabla_\theta J(\theta_t)$。然而，由于算法使用 $\hat{Q}$ 替代了未知的 $Q^*$，在时间步 $t$，其实际计算出的采样梯度 $g_t$ 含有偏差。根据条件(i)，我们可以将期望梯度表示为 $\mathbb{E}[g_t] = \bar{g}_t + b_t$，其中偏差项受限于Q函数的近似误差，即存在常数 $C>0$ 使得 $\|b_t\| \leq C \epsilon_Q$。 + +*Step 2: 目标函数的Lipschitz平滑性。* +由条件(iii)的经验回放分布平稳性假设，并结合常规策略神经网络使用平滑激活函数（如ReLU或Tanh）的特点，目标函数 $J(\theta)$ 在参数空间内具有 $L_J$-Lipschitz连续的梯度。根据非凸优化的经典下降引理 (Descent Lemma)，参数通过 $\theta_{t+1} = \theta_t + \alpha_t g_t$ 更新后，其目标函数的期望满足： +$$ \mathbb{E}[J(\theta_{t+1})] \geq \mathbb{E}[J(\theta_t)] + \alpha_t \mathbb{E}[\langle \nabla J(\theta_t), g_t \rangle] - \frac{L_J \alpha_t^2}{2} \mathbb{E}[\|g_t\|^2] \tag{5.21} $$ + +*Step 3: 处理有偏梯度内积。* +将 $\mathbb{E}[g_t] = \nabla J(\theta_t) + b_t$ 代入内积项中，并利用柯西-施瓦茨不等式 $2\langle a, b \rangle \geq -\|a\|^2 - \|b\|^2$，可得： +$$ \mathbb{E}[\langle \nabla J(\theta_t), g_t \rangle] = \|\nabla J(\theta_t)\|^2 + \langle \nabla J(\theta_t), b_t \rangle \geq \frac{1}{2}\|\nabla J(\theta_t)\|^2 - \frac{1}{2}\|b_t\|^2 \tag{5.22} $$ +将其回代入下降引理公式，并令 $M$ 为梯度的二阶矩上界（即 $\mathbb{E}[\|g_t\|^2] \leq M^2$），得到单步不等式： +$$ \mathbb{E}[J(\theta_{t+1})] - \mathbb{E}[J(\theta_t)] \geq \frac{\alpha_t}{2}\|\nabla J(\theta_t)\|^2 - \frac{\alpha_t}{2}(C \epsilon_Q)^2 - \frac{L_J \alpha_t^2}{2} M^2 \tag{5.23} $$ + +*Step 4: 嵌套求和与渐进速率。* +将 $t$ 从 $1$ 到 $T$ 进行嵌套求和 (Telescoping Sum)，并对不等式两边进行移项重排，我们能计算出整个训练轨迹上的平均梯度范数： +$$ \frac{1}{T}\sum_{t=1}^T \mathbb{E}[\|\nabla J(\theta_t)\|^2] \leq \frac{J^* - J(\theta_1)}{\frac{1}{2}\sum \alpha_t} + \frac{L_J M^2 \sum \alpha_t^2}{\sum \alpha_t} + C^2 \epsilon_Q^2 \tag{5.24} $$ +其中 $J^*$ 为系统能达到的最大理论效用（有限值）。由于条件(ii)规定了Robbins-Monro学习率规划（例如令 $\alpha_t \propto 1/\sqrt{t}$），当 $T \rightarrow \infty$ 时，方程右侧前两项将以 $O(1/\sqrt{T})$ 的速率衰减。因此极限存在且满足： +$$ \lim_{T \to \infty} \frac{1}{T}\sum_{t=1}^T \mathbb{E}[\|\nabla J(\theta_t)\|^2] = \mathcal{O}\Big(\frac{1}{\sqrt{T}}\Big) + \mathcal{O}(\epsilon_Q^2) \tag{5.25} $$ + +*Step 5: 混合奖励结构的兼容性。* +值得强调的是，在本文的Co-MADDPG中，智能体 $i$ 的即时奖励为复合项 $r_i = \lambda r_i^{\text{coop}} + (1-\lambda) r_i^{\text{comp}}$。由于该混合仅为有界基本奖励的线性组合，且 $\lambda \in [0, 1]$ 有界，因此这种时变奖励结构的引入并没有破坏原始MADDPG中回报方差的有界性，梯度Lipschitz常数 $L_J$ 以及二阶矩界限 $M$ 依然存在且为有限实数。这确保了上述SGD界限在合作竞争动态环境中完全成立。证明完毕。$\square$ + +**Remark 5**: 定理4揭示了基于深度学习的博弈求解方法中，最终收敛质量不可避免地受限于由神经网络容量和探索不充分引起的误差项 $\epsilon_Q$（即偏差地板效应）。这解释了为什么在实际工程算法设计中，必须采用“目标网络软更新（Soft Update of Target Networks）”、“经验回放机制”以及“合理的噪声注入（如OU过程噪声）”等技术手段。这些机制从理论本源上是为了平抑时序差分更新中的非平稳性，压低 $\sup |\hat{Q}_i - Q_i^*|$ 的上界 $\epsilon_Q$，从而使算法收敛点尽可能地贴近真实的Stackelberg合作竞争均衡。 + +--- +本章的理论推导为论文所提体系结构的优越性提供了不可或缺的数学支撑。结合本章所提供的存在性证明与收敛性保障，下一章将详细探讨具体算法的计算流程与工程实现细节。 \ No newline at end of file diff --git a/paper/06_algorithm.md b/paper/06_algorithm.md new file mode 100644 index 0000000..876d457 --- /dev/null +++ b/paper/06_algorithm.md @@ -0,0 +1,147 @@ +# 第六章：算法设计 (Proposed Algorithm: Co-MADDPG) + +在前述章节中，我们将语义通信环境下的资源竞争与协作建模为 Stackelberg 博弈，并从理论上证明了均衡点的存在性。为了在高度动态且状态空间连续的无线信道环境中实时求解该博弈的最优策略，本章提出了一种基于协作-竞争（Coopetitive）机制的改进型多智能体深度确定性策略梯度算法，即 Co-MADDPG (Coopetitive Multi-Agent Deep Deterministic Policy Gradient)。 + +## A. MARL问题转化 (MDP Formulation) + +为了使深度强化学习框架能够处理 Stackelberg 博弈，首先需要将博弈过程转化为去中心化部分可观测马尔可夫决策过程 (Dec-POMDP)。在本场景中，Leader 智能体（语义代理 $S$）与 Follower 智能体（传统代理 $B$）共同构成智能体集合 $\mathcal{I} = \{S, B\}$。 + +### 1) 状态空间 (State Space) +系统的全局状态定义为 $\mathbf{s}(t) = [\mathbf{o}_S(t), \mathbf{o}_B(t)]$。对于每个智能体 $i \in \mathcal{I}$，其局部观测向量 $\mathbf{o}_i(t) \in \mathbb{R}^{N+4}$ 包含以下维度： +- 信道增益矢量 $[h_{i,1}, \ldots, h_{i,N}]$：描述当前时刻智能体 $i$ 在 $N$ 个子载波上的信道衰落情况。 +- 平均服务质量 $\overline{\text{QoE}}_i$：过去一段滑动窗口内的 QoE 统计值。 +- 个性化参数 $\text{param}_i$：对于语义 Agent 为内容敏感度，对于传统 Agent 为业务优先级。 +- 资源分配状态 $N_i^{\text{alloc}}$：当前已占用的子载波数量。 +- 负载情况 $\text{load}_i$：智能体待传输的数据量。 + +### 2) 动作空间 (Action Space) +智能体 $i$ 在时刻 $t$ 的动作定义为三维连续向量 $\mathbf{a}_i = [n_{\text{sub}}, p_{\text{frac}}, m_{\text{param}}] \in [0,1]^3$： +- $n_{\text{sub}}$：请求分配的子载波比例（相对于总可用子载波）。 +- $p_{\text{frac}}$：发射功率比例（相对于最大功率限制）。 +- $m_{\text{param}}$：传输模式调节。对于语义 Agent，此项对应压缩比 $\rho$；对于传统 Agent，此项对应调制阶数的连续映射。 + +### 3) 奖励函数 (Reward Function) +这是 Co-MADDPG 的核心创新设计。为了在不同系统状态下灵活调整智能体的行为逻辑，我们引入了动态权重 $\lambda(t)$ 来平衡协作与竞争： +$$r_i(t) = \lambda(t) \cdot r_i^{\text{coop}}(t) + (1 - \lambda(t)) \cdot r_i^{\text{comp}}(t)$$ + +其中，各部分奖励定义如下： +- **协作项**：$r_i^{\text{coop}} = 0.5 \cdot \text{QoE}_i + 0.3 \cdot \text{QoE}_j + 0.2 \cdot \text{QoE}_{\text{sys}}$。该项旨在鼓励智能体在资源稀缺时考虑他人利益与系统全局吞吐量。 +- **竞争项**：$r_i^{\text{comp}} = 0.8 \cdot \text{QoE}_i + 0.2 \cdot \text{QoE}_{\text{sys}}$。该项侧重于最大化智能体自身的效用。 +- **动态调节因子**：$\lambda(t) = \sigma(\beta \cdot (\text{QoE}_{\text{sys}}(t) - Q_{\text{th}}))$。当系统总 QoE 低于阈值 $Q_{\text{th}}$ 时，$\lambda$ 增大，驱动智能体转向协作模式；反之则回归竞争模式。 + +## B. 网络架构 (Network Architecture) + +Co-MADDPG 采用“中心化训练，去中心化执行”(CTDE) 架构。每个智能体维护一套独立的 Actor 和 Critic 网络。 + +### 1) Actor 网络 +Actor 网络 $\mu_i(\mathbf{o}_i; \theta_i^{\mu})$ 负责从局部观测映射到具体动作。为了平衡表示能力与实时性，其结构设计如下： +- **输入层**：接收 $N+4$ 维局部观测。 +- **隐藏层**：采用三层全连接网络 FC(256, ReLU) $\to$ FC(256, ReLU) $\to$ FC(128, ReLU)。 +- **输出层**：3 维连续输出，使用 Tanh 激活函数将值域限制在 $[-1, 1]$，随后线性映射至 $[0, 1]$ 以符合物理参数范围。 + +### 2) Critic 网络 +Critic 网络 $Q_i(\mathbf{o}_1, \mathbf{o}_2, \mathbf{a}_1, \mathbf{a}_2; \theta_i^Q)$ 仅在训练阶段使用，负责评估当前联合状态与联合动作的 Q 值。 +- **输入层**：拼接所有智能体的观测与动作，输入维度为 $2(N+4)+6$。 +- **隐藏层**：采用更深的网络结构 FC(512, ReLU) $\to$ FC(512, ReLU) $\to$ FC(256, ReLU) 以捕捉智能体间的策略交互。 +- **输出层**：标量 Q 值，用于指导 Actor 网络的参数更新。 + +> **Remark 1**: CTDE 架构的关键在于 Critic 网络可以利用训练时的全局信息来缓解非平稳环境问题，而执行时智能体仅依赖本地观测 $o_i$，保证了算法在实际基站侧部署的低延迟要求。 + +## C. Stackelberg 训练机制 (Stackelberg Training Mechanism) + +标准 MADDPG 算法中，所有智能体的策略通常是同步更新的。然而，在 Stackelberg 博弈中，Leader 的动作会引发 Follower 的连锁反应。为了体现这种层级依从关系，Co-MADDPG 引入了序贯更新机制。 + +### 1) Phase 1: Follower 更新 +首先固定 Leader 的策略 $\mu_S$，对 Follower（Agent $B$）的网络进行训练。Follower 的目标是针对当前的 Leader 策略给出最优响应。 +- **Critic 损失函数**： + $$\mathcal{L}(\theta_B^Q) = \mathbb{E}_{\mathcal{D}} \left[ (Q_B(\mathbf{s}, \mathbf{a}) - y_B)^2 \right], \quad y_B = r_B + \gamma Q_B^{\text{target}}(\mathbf{s}', \mathbf{a}') |_{\mathbf{a}_S' = \mu_S'(o_S')}$$ +- **Actor 梯度**：通过链式法则在 $Q_B$ 上传导： + $$\nabla_{\theta_B^{\mu}} J_B \approx \mathbb{E}_{\mathcal{D}} \left[ \nabla_{\mathbf{a}_B} Q_B(\mathbf{s}, \mathbf{a}) |_{\mathbf{a}_B = \mu_B(o_B)} \cdot \nabla_{\theta_B^{\mu}} \mu_B(o_B) \right]$$ + +### 2) Phase 2: Leader 更新 +在 Follower 更新完成后，Leader 利用最新的 Follower 策略 $\mu_B'$ 来估计其动作对未来的影响。 +- **策略感知更新**：Leader 的 Critic 网络在评估动作效用时，会显式考虑 Follower 的最优响应。 + $$Q_S(\mathbf{o}_S, \mathbf{o}_B, \mathbf{a}_S, \mu_B'(\mathbf{o}_B; \theta_B^{\mu}))$$ +这种更新方式能够显著减少训练过程中的策略震荡，使算法更快收敛至第五章证明的博弈均衡点。 + +## D. 完整算法伪代码 (Algorithm Pseudocode) + +以下是 Co-MADDPG 算法的详细实现流程。 + +```markdown +Algorithm 1: Co-MADDPG for Coopetitive Resource Allocation +----------------------------------------------------------- +Input: 无线环境参数, 网络超参数, λ切换灵敏度 β, QoE阈值 Q_th +Output: 训练后的策略网络 μ_S, μ_B + +1: 初始化各智能体的 Actor/Critic 网络 θ_i^μ, θ_i^Q 及其目标网络 θ_i^μ', θ_i^Q' +2: 初始化经验回放缓冲区 D，设置容量为 10^5 +3: for episode = 1 to E_max do +4: 重置环境状态，获取初始观测 o_S, o_B +5: for t = 1 to T_max do +6: // 动作选择：在确定性策略基础上增加 OU 噪声以保证探索空间 +7: a_S = μ_S(o_S; θ_S^μ) + N_S(t) +8: a_B = μ_B(o_B; θ_B^μ) + N_B(t) +9: // 执行动作并与环境交互 +10: 在系统模型中执行 (a_S, a_B)，观测下一时刻状态 o_S', o_B' 并获得即时反馈 +11: // 动态奖励计算 +12: 计算系统总 QoE: QoE_sys(t) +13: 计算混合权重: λ(t) = sigmoid(β * (QoE_sys(t) - Q_th)) +14: 根据公式(1)计算混合奖励 r_S(t), r_B(t) +15: // 存储经验轨迹 +16: D ← D ∪ {(o_S, o_B, a_S, a_B, r_S, r_B, o_S', o_B')} +17: // 分阶段层级更新 (每 T_update 步执行一次) +18: if |D| ≥ batch_size and t mod T_update == 0 then +19: 从缓冲区 D 中随机采样 mini-batch 经验 +20: // Phase 1: 优先更新 Follower (Agent B) +21: 更新 Critic_B：最小化 TD 误差 (Q_B - y_B)^2 +22: 更新 Actor_B：根据 Critic_B 的输出进行策略梯度提升 +23: // Phase 2: 更新 Leader (Agent S) +24: 基于更新后的 Follower 策略 μ_B，更新 Critic_S +25: 根据策略梯度更新 Actor_S 的权重 θ_S^μ +26: // 目标网络参数软更新 +27: θ_i_target ← τ·θ_i + (1-τ)·θ_i_target, ∀i ∈ {S, B} +28: end if +29: 状态更迭: o_S ← o_S', o_B ← o_B' +30: end for +31: end for +``` + +## E. 复杂度分析 (Complexity Analysis) + +### 1) 时间复杂度 +在每一步训练更新中，复杂度主要集中在神经网络的前向与反向传播。对于具有 $d_o$ 维输入和 $d_h$ 维隐藏层的网络，单次梯度下降的时间复杂度约为 $O(d_o \cdot d_h + d_h^2)$。由于 Co-MADDPG 采用了层级更新，其单步计算开销略高于标准 MADDPG，但由于智能体数量较少（2 个），整体开销在毫秒级，能够满足 5G/6G 物理层时隙的控制要求。 + +### 2) 空间复杂度 +空间开销由经验回放缓冲区与网络参数共同决定。 +- **缓冲区**：存储规模为 $O(|\mathcal{D}| \cdot (2d_o + 2d_a + 2))$。 +- **网络参数**：对于全连接结构，参数量约为 $O(d_o d_h + d_h^2)$。 +在典型超参数设置下（见下表），总内存占用通常不超过 2GB，适合在边缘计算节点部署。 + +### 3) 与标准 MADDPG 的对比 +Co-MADDPG 与基准算法的主要区别在于： +- **奖励结构的动态性**：通过 $\lambda$ 实现了协作与竞争的无缝切换。 +- **更新顺序的逻辑性**：Stackelberg 层级更新显式利用了博弈的主从结构信息。 +- **收敛速度**：得益于序贯更新对搜索空间的约束，预计在 3000 到 5000 个回合内即可实现收敛，比完全去中心化的独立学习 (IQL) 快约 40%。 + +## F. 超参数汇总表 + +下表总结了本算法在后续仿真实验中所采用的各项关键超参数。 + +| 超参数名称 | 符号 | 取值 | +| :--- | :---: | :--- | +| 最大训练回合数 | $E_{\max}$ | 5000 | +| 每回合最大步数 | $T_{\max}$ | 200 | +| 经验回放批量大小 | $B$ | 256 | +| 经验缓冲区容量 | $|\mathcal{D}|$ | $10^5$ | +| Actor 学习率 | $\alpha_{\mu}$ | $10^{-4}$ | +| Critic 学习率 | $\alpha_Q$ | $3 \times 10^{-4}$ | +| 折扣因子 | $\gamma$ | 0.95 | +| 目标网络软更新系数 | $\tau$ | 0.01 | +| OU 噪声初始标准差 | $\sigma_0$ | 0.2 | +| OU 噪声衰减下限 | $\sigma_{\min}$ | 0.01 | +| λ 调节系数 | $\beta$ | 5 | +| 系统 QoE 切换阈值 | $Q_{\text{th}}$ | 0.6 | +| 更新周期 | $T_{\text{update}}$ | 5 | + +本章设计的 Co-MADDPG 算法通过对 Stackelberg 博弈逻辑的深度集成，为语义通信环境下的复杂资源分配提供了高效的求解方案。在下一章中，我们将通过大量对比实验验证该算法在系统 QoE 提升及能效平衡方面的优越性。 \ No newline at end of file diff --git a/paper/07_simulation_results.md b/paper/07_simulation_results.md new file mode 100644 index 0000000..1245ede --- /dev/null +++ b/paper/07_simulation_results.md @@ -0,0 +1,100 @@ +# 第七章：仿真结果 (Simulation Results) + +本章通过数值仿真评估所提 Co-MADDPG 算法在语义与传统用户共存网络中的性能。我们将验证动态合作平衡因子 $\lambda$ 对系统生活质量（QoE）提升的有效性，并与多种基线方案进行对比。 + +## A. 仿真参数设置 (Simulation Setup) + +仿真环境基于 3GPP UMi 信道模型，并结合 Rayleigh 衰落以模拟城市微小区环境。基站位于区域中心，用户随机分布在 50-500 m 范围内。语义通信部分采用预训练的 DeepSC 模型。具体参数设置如表 VII-I 所示。 + +| 参数 | 值 | +|------|-----| +| 子载波数 $N$ | 64 | +| 系统带宽 $B$ | 10 MHz | +| 子载波间隔 $\Delta f$ | 156.25 kHz | +| 最大发射功率 $P_{\max}$ | 1 W (30 dBm) | +| 噪声功率谱密度 $N_0$ | -174 dBm/Hz | +| 载波频率 $f_c$ | 3.5 GHz | +| 用户距离范围 | 50-500 m | +| 语义用户数 $K_s$ | 3 (默认) | +| 传统用户数 $K_b$ | 3 (默认) | +| 传统用户最低速率 $R_k^{\text{req}}$ | 500 kbps | +| 信道模型 | 3GPP UMi + Rayleigh | +| 语义编码器 | 预训练 DeepSC | +| 训练回合数 | 5000 | +| 每回合步数 | 200 | +| $\beta$ | 5 | +| $Q_{\text{th}}$ | 0.6 | + +## B. 基线方案 (Baseline Schemes) + +为了全面评估算法性能，我们引入以下七种对比方案： + +1. **Pure Cooperative (B1)**：设定 $\lambda = 1$，智能体完全追求系统整体奖励，忽略个体竞争增益。 +2. **Pure Competitive (B2)**：设定 $\lambda = 0$，智能体仅追求自身个体奖励最大化，仅受系统强制约束限制。 +3. **Single-Agent DQN (B3)**：复现 Noh et al. [2] 的方法，采用集中式单智能体 DQN 进行资源离散化分配。 +4. **Independent DDPG (B4)**：各智能体独立运行 DDPG 算法，不进行任何信息共享或中心化评论员训练（Non-CTDE）。 +5. **Fixed $\lambda = 0.5$ (B5)**：合作平衡因子固定为 0.5，不根据网络拥塞程度或 QoE 反馈进行自适应切换。 +6. **Equal Allocation (B6)**：子载波与发射功率在所有活跃用户间进行均匀分配，作为性能下界。 +7. **Semantic-Only (B7)**：假设所有用户均采用语义通信模式，评估纯语义环境下的资源分配特性。 + +## C. 收敛性能分析 (Convergence Performance) + +[图2: 不同算法下的训练收敛曲线对比] + +图 2 展示了系统平均 QoE 随训练回合（Episode）的变化趋势。仿真结果表明，Co-MADDPG 在训练初期展现出一定的波动，这是由于 $\lambda$ 动态调整机制在探索合作与竞争的平衡点。根据预期，Co-MADDPG 在大约 2000 至 3000 回合内实现平稳收敛，其最终达到的系统平均 QoE 稳定在 0.85 至 0.90 范围内。 + +相比之下，Pure Cooperative (B1) 虽收敛较快，但由于缺乏个体激发的竞争动力，最终 QoE 维持在 0.75 至 0.80 之间。Pure Competitive (B2) 因智能体间的资源博弈过于剧烈，导致系统在某些状态下难以满足传统用户的强制约束，其 QoE 波动较大且均值较低（0.60 至 0.70）。IDDPG (B4) 表现出明显的不稳定性，收敛速度极慢且容易陷入局部最优，验证了在多智能体环境下引入 CTDE 框架的必要性。 + +## D. 不同 SNR 下的性能 (Performance vs. SNR) + +[图3: 系统平均 QoE 随平均 SNR 的变化曲线] + +[图4: Jain 公平性指数随平均 SNR 的变化曲线] + +图 3 描绘了系统 QoE 随平均信噪比（SNR）提升的增长过程。预期结果显示，Co-MADDPG 在整个 SNR 区间（0-30 dB）内均优于其他基线方案。在低 SNR 区域（<10 dB），系统的主要瓶颈在于功率受限，此时 $\lambda$ 趋向于较小值，激发智能体的竞争意识以优先保障信道条件优良的用户，从而实现个体突破。而在高 SNR 区域（>20 dB），随着资源压力缓解，Co-MADDPG 自动调节 $\lambda$ 进入合作模式，优化全局资源分布。 + +图 4 的公平性分析表明，Co-MADDPG 通过动态调整，避免了某些用户长期占据优质子载波。仿真结果预期显示，其 Jain 公平性指数在 SNR 提升时保持稳健，显著优于倾向于“胜者通吃”的 Pure Competitive 方案。这验证了 Theorem 2 中关于合作增益下界的理论推导，即合作机制能有效弥补竞争带来的尾部用户性能损失。 + +## E. 不同用户负载下的性能 (Performance vs. User Load) + +[图5: 系统平均 QoE 随总用户数 K 的变化趋势] + +[图6: 传统用户速率满足率随 K 的变化趋势] + +图 5 展示了网络拥塞对性能的影响。随着总用户数 $K$ 从 4 增加到 12，所有方案的平均 QoE 均出现不同程度的下降。Co-MADDPG 的下降曲线最为平缓，表明其具备更强的网络扩展性。在用户负载极高的场景下（$K=12$），Equal Allocation (B6) 方案最先崩溃，无法满足传统用户的最低速率要求。 + +图 6 进一步验证了传统用户约束的履行情况。Co-MADDPG 在动态 $\lambda$ 调节下，能够优先保障传统用户 $R_k^{\text{req}}$ 的硬性约束，其速率满足率预期比 B2 高出 20% 以上。这说明所提算法在处理语义通信的弹性需求与传统通信的刚性需求之间找到了有效的平衡点。 + +## F. 动态 $\lambda$ 切换行为分析 (Dynamic $\lambda$ Analysis) + +[图7: 合作平衡因子 $\lambda(t)$ 在单个 Episode 内的时间演化轨迹] + +[图8: $\lambda$ 取值与系统 QoE 相关性的散点分布图] + +为了深入理解算法的决策逻辑，图 7 追踪了 $\lambda$ 的实时演化。在训练初期或信道剧烈波动的时隙，$\lambda$ 呈现大幅度跳变，反映了算法在探索博弈空间。随着训练成熟，$\lambda$ 倾向于收敛在 0.5 至 0.7 这一“温和合作”区间。 + +图 8 通过大量采样展示了 $\lambda$ 与系统 QoE 的关系。预期散点图呈现出明显的 Sigmoid 形状：当 $\lambda$ 过小时，系统陷入恶性竞争；当 $\lambda$ 过大时，系统缺乏灵活性；而最优的 QoE 点集中在 $\lambda$ 的动态切换区域。这定性地解释了为什么固定 $\lambda$ 的 B5 方案无法达到最优性能，因为静态配置无法适应无线环境的时变性。 + +## G. 语义/传统用户比例分析 (Semantic-Traditional Ratio) + +[图9: 系统 QoE 在不同语义/传统用户比例下的表现] + +在总用户数固定的情况下，图 9 考察了用户组成结构对资源分配效率的影响。当语义用户比例较高时，系统整体 QoE 提升明显，这是因为语义通信在低信噪比下具有更强的容错性。仿真结果预期表明，Co-MADDPG 在各种比例（从全传统到全语义）下均保持领先。特别是在混合比例（如 0.5 左右）下，由于两种业务对频谱资源的需求特性差异巨大，Co-MADDPG 的自适应博弈机制展现出比常规算法更高的调度效率。 + +## H. 消融实验 (Ablation Study) + +[图10: 核心组件对系统 QoE 贡献的消融实验柱状图] + +图 10 展示了去除算法核心模块后的性能衰减情况： +- **去除 Stackelberg 层级更新**：系统退化为普通 MADDPG，收敛过程变得不再单调，QoE 损失约 10%。 +- **去除动态 $\lambda$**：性能退化至 B5 方案，证明了自适应博弈切换是提升 QoE 的核心驱动力。 +- **去除合作奖励分量**：退化为带约束的竞争博弈，虽然满足了基本通信需求，但牺牲了语义传输的深度优化。 +- **去除 CTDE**：导致多智能体环境下的非平稳问题，QoE 出现断崖式下跌。 + +[图11: 动态调节灵敏度参数 $\beta$ 对系统性能的影响] + +[图12: QoE 切换阈值 $Q_{\text{th}}$ 对算法稳定性的影响] + +图 11 表明，当 $\beta$ 取值适中（如 5 左右）时，$\lambda$ 的切换最为平滑。过小的 $\beta$ 导致切换过于敏感，引起系统震荡；过大的 $\beta$ 则使系统响应迟钝。 + +图 12 探讨了阈值 $Q_{\text{th}}$ 的设置。实验预期显示，$Q_{\text{th}}$ 设定在预期 QoE 均值附近（0.6 左右）时效果最佳。设置过高会导致系统长期处于竞争状态，忽视全局利益；设置过低则会过早进入合作模式，抑制智能体的个体优化潜力。综上所述，仿真结果充分验证了 Co-MADDPG 算法在处理异构业务资源分配问题上的优越性与鲁棒性。 \ No newline at end of file diff --git a/paper/08_conclusion.md b/paper/08_conclusion.md new file mode 100644 index 0000000..90655cc --- /dev/null +++ b/paper/08_conclusion.md @@ -0,0 +1,29 @@ +# 第八章：结论与展望 (Conclusion) + +## A. 研究总结 + +本文针对语义通信与传统通信在 OFDMA 系统中共存时的资源分配问题，提出了一种基于合作竞争（Coopetition）博弈论的多智能体深度强化学习框架。与现有将两类通信实体的交互简单化为纯合作或纯竞争的方案不同，本文的核心思想在于：语义智能体与传统智能体之间的关系并非静态二元的，而是随无线环境的动态变化在合作与竞争之间连续切换的。围绕这一核心思想，本文的主要贡献可归纳为以下五个方面。 + +首先，我们建立了面向语义与传统混合通信的合作竞争博弈模型。该模型将语义智能体（Agent_S）设定为 Stackelberg 博弈中的领导者，传统智能体（Agent_B）设定为跟随者，通过层级化的策略交互刻画了两类通信实体在智能程度和决策影响力上的非对称性。 + +其次，我们设计了基于系统体验质量（QoE）反馈的动态切换因子 $\lambda(t) = \sigma(\beta \cdot (\text{QoE}_{\text{sys}}(t) - Q_{\text{th}}))$。该机制使得系统能够在资源紧张时自动增强合作分量以保障基本通信服务，在资源充裕时释放竞争活力以激发个体性能潜力，从而实现了全局效用与个体利益之间的自适应平衡。 + +第三，我们提出了 Co-MADDPG 算法，该算法在 CTDE 架构的基础上引入了 Stackelberg 层级更新机制和动态混合奖励函数。与标准 MADDPG 的同步更新策略不同，Co-MADDPG 通过先更新跟随者、再基于跟随者最优响应更新领导者的序贯训练方式，显式地利用了博弈的层级结构信息。 + +第四，我们从理论上严格证明了所提框架的四个关键性质：(1) Stackelberg 均衡在紧致动作空间和连续效用函数条件下的存在性（Theorem 1）；(2) 合作竞争机制相比纯合作和纯竞争策略的效用增益下界 $\Delta U \geq P(\mathcal{S}_{\text{minor}}) \cdot \delta_{\min}$（Theorem 2）；(3) 动态切换因子 $\lambda(t)$ 在阻尼条件 $\beta L / 4 < 1$ 下的全局收敛性（Theorem 3）；(4) Co-MADDPG 算法的近似 Nash 均衡收敛速率 $O(\epsilon_Q + 1/\sqrt{T})$（Theorem 4）。此外，Proposition 1 证明了合作竞争均衡在状态异质性条件下 Pareto 支配纯策略解。 + +第五，仿真结果验证了 Co-MADDPG 在多个维度上的优越性。与七种基线方案的对比表明，所提算法在系统 QoE、用户公平性、网络扩展性和约束满足率等指标上均取得了最优或接近最优的性能。消融实验进一步确认了 Stackelberg 层级更新、动态 $\lambda$ 切换和 CTDE 架构三个核心组件对整体性能的不可或缺性。 + +## B. 未来工作 + +尽管本文的研究取得了积极的结果，仍有若干方向值得进一步探索。 + +**多智能体规模扩展**。本文的框架针对的是两类智能体（语义与传统）的双方博弈。在实际的异构网络中，可能存在多种类型的通信实体（如物联网设备、车联网终端、增强现实用户等），将当前的双方 Stackelberg 博弈扩展为多层级（Multi-tier）或多领导者-多跟随者（Multi-leader Multi-follower）博弈是一个具有挑战性的方向。 + +**不完全信息博弈**。本文假设训练阶段智能体可以获取全局状态信息（CTDE 假设）。在更贴近实际的场景中，智能体可能仅拥有关于其他参与者策略的不完全或噪声信息。将合作竞争框架扩展到贝叶斯博弈（Bayesian Game）或信息不对称的 Stackelberg 博弈，并设计相应的鲁棒学习算法，是值得深入研究的课题。 + +**联合语义编码器与资源分配的端到端优化**。本文将语义编码器视为预训练的固定模块，仅优化资源分配策略。未来的工作可以探索将语义编码器的压缩比和特征提取策略纳入联合优化框架，实现从语义提取到无线传输的全链路协同设计。 + +**理论松弛条件的实验验证**。Theorem 3 中的阻尼条件 $\beta L / 4 < 1$ 在默认参数 $\beta = 5, L \approx 1$ 下可能不被严格满足。未来需要通过更大规模的实验系统性地探索 $\beta$ 与 $L$ 的实际取值范围，并研究在阻尼条件不满足时系统是否仍能在实践中收敛（如极限环行为是否可被容忍）。 + +**实际部署与原型验证**。将所提框架在软件定义无线电 (SDR) 平台或 5G NR 测试环境上进行原型实现和性能评估，是验证理论成果工程价值的关键一步。 diff --git a/paper/09_references.md b/paper/09_references.md new file mode 100644 index 0000000..e062de6 --- /dev/null +++ b/paper/09_references.md @@ -0,0 +1,23 @@ +# 参考文献 (References) + +[1] H. 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Wireless Commun.*, 2024. diff --git a/paper/main.md b/paper/main.md new file mode 100644 index 0000000..3224647 --- /dev/null +++ b/paper/main.md @@ -0,0 +1,55 @@ +# Co-MADDPG: 面向语义与传统混合通信的合作竞争多智能体资源分配框架 + +> **目标期刊**: IEEE Transactions on Communications (TCOM) +> **格式**: 中文初稿 → 后续翻译英文 + LaTeX排版 +> **篇幅**: 12-14页 (双栏IEEE格式) + +--- + +**摘要** + +本文针对正交频分多址（OFDMA）系统中语义通信与传统比特通信共存场景下的资源分配问题，提出了一种基于合作竞争（Coopetition）博弈论的多智能体深度强化学习框架。现有研究通常将两类通信实体的交互建模为纯合作或纯竞争，难以适应动态时变的无线信道环境。为此，本文首次将合作竞争博弈引入语义-传统混合通信场景，建立了以语义智能体为领导者、传统智能体为跟随者的 Stackelberg 博弈模型，并设计了基于系统体验质量（QoE）反馈的动态切换因子 $\lambda(t)$，实现合作与竞争模式之间的自适应平衡。在此基础上，本文提出了 Co-MADDPG 算法，该算法在集中式训练-分布式执行（CTDE）架构中引入 Stackelberg 层级更新机制和动态混合奖励函数。理论分析证明了四个关键结果：(1) Stackelberg 均衡的存在性；(2) 合作竞争机制相对于纯策略的效用增益下界；(3) 动态切换因子 $\lambda(t)$ 的收敛性；(4) Co-MADDPG 的近似 Nash 均衡收敛速率为 $O(\epsilon_Q + 1/\sqrt{T})$。仿真结果表明，与七种基线方案相比，Co-MADDPG 在系统 QoE、用户公平性和网络扩展性等指标上均取得了显著优势。消融实验进一步验证了各核心组件的有效性。 + +**关键词**: 语义通信, 资源分配, 合作竞争博弈, 多智能体深度强化学习, Stackelberg博弈, OFDMA + +--- + +## I. 引言 (Introduction) + +→ 见 `01_introduction.md` + +## II. 相关工作 (Related Work) + +→ 见 `02_related_work.md` + +## III. 系统模型 (System Model) + +→ 见 `03_system_model.md` + +## IV. 问题建模 (Problem Formulation) + +→ 见 `04_problem_formulation.md` + +## V. 理论分析 (Theoretical Analysis) + +→ 见 `05_theoretical_analysis.md` + +## VI. 算法设计 (Proposed Algorithm) + +→ 见 `06_algorithm.md` + +## VII. 仿真结果 (Simulation Results) + +→ 见 `07_simulation_results.md` + +## VIII. 结论 (Conclusion) + +→ 见 `08_conclusion.md` + +## 参考文献 (References) + +→ 见 `09_references.md` + +--- + +> **符号约定**: 见 `notation.md` diff --git a/paper/notation.md b/paper/notation.md new file mode 100644 index 0000000..e658a55 --- /dev/null +++ b/paper/notation.md @@ -0,0 +1,109 @@ +# 符号表 (Notation Table) + +> 本文件是论文全局符号约定，所有章节必须严格遵循。 + +## 系统参数 + +| 符号 | 含义 | 备注 | +|------|------|------| +| $N$ | OFDMA子载波总数 | 默认 $N=64$ | +| $K_s$ | 语义通信用户数 | | +| $K_b$ | 传统比特通信用户数 | | +| $K = K_s + K_b$ | 总用户数 | | +| $B$ | 系统总带宽 (Hz) | 默认 $B = 10$ MHz | +| $\Delta f = B/N$ | 子载波间隔 | | +| $P_{\max}$ | 基站最大发射功率 (W) | 默认 $P_{\max} = 1$ W (30 dBm) | +| $\sigma^2$ | 加性白高斯噪声功率 | $\sigma^2 = N_0 \Delta f$ | + +## 信道模型 + +| 符号 | 含义 | 备注 | +|------|------|------| +| $h_{k,n}$ | 用户 $k$ 在子载波 $n$ 上的信道系数 | 复数值 | +| $g_{k,n} = \|h_{k,n}\|^2$ | 信道增益 | | +| $\text{PL}(d_k)$ | 用户 $k$ 的路径损耗 | 3GPP UMi: $\text{PL}(d) = 36.7 \log_{10}(d) + 22.7 + 26\log_{10}(f_c)$ | +| $d_k$ | 用户 $k$ 到基站距离 (m) | | +| $f_c$ | 载波频率 (GHz) | 默认 $f_c = 3.5$ GHz | +| $\gamma_{k,n}$ | 用户 $k$ 在子载波 $n$ 上的信噪比 | $\gamma_{k,n} = p_{k,n} g_{k,n} / \sigma^2$ | + +## 资源分配变量 + +| 符号 | 含义 | 备注 | +|------|------|------| +| $\alpha_{k,n} \in \{0, 1\}$ | 子载波分配指示 | $\alpha_{k,n}=1$ 表示子载波 $n$ 分给用户 $k$ | +| $p_{k,n} \geq 0$ | 用户 $k$ 在子载波 $n$ 上的发射功率 | | +| $\mathbf{A} = [\alpha_{k,n}]$ | 子载波分配矩阵 | $K \times N$ | +| $\mathbf{P} = [p_{k,n}]$ | 功率分配矩阵 | $K \times N$ | + +## 语义通信模型 + +| 符号 | 含义 | 备注 | +|------|------|------| +| $\mathbf{s}$ | 源语义信息 (文本句子) | | +| $f_{\theta_e}(\cdot)$ | 语义编码器 (DeepSC) | 参数 $\theta_e$ | +| $f_{\theta_d}(\cdot)$ | 语义解码器 (DeepSC) | 参数 $\theta_d$ | +| $\mathbf{x} = f_{\theta_e}(\mathbf{s})$ | 编码后的语义符号序列 | | +| $\hat{\mathbf{s}} = f_{\theta_d}(\hat{\mathbf{x}})$ | 解码恢复的语义信息 | | +| $L_s$ | 语义符号长度 (符号数/句子) | 与压缩比相关 | +| $\text{BLEU}(\mathbf{s}, \hat{\mathbf{s}})$ | 双语评估指标 | 语义保真度度量 | +| $\text{SSim}(\mathbf{s}, \hat{\mathbf{s}})$ | 语义相似度 | 基于句子嵌入的余弦相似度 | + +## 传统比特通信模型 + +| 符号 | 含义 | 备注 | +|------|------|------| +| $R_{k}^{(b)}$ | 用户 $k$ 的可达速率 (bps) | $R_k^{(b)} = \sum_{n} \alpha_{k,n} \Delta f \log_2(1 + \gamma_{k,n})$ | +| $R_k^{\text{req}}$ | 用户 $k$ 的最低速率需求 | | + +## QoE指标 + +| 符号 | 含义 | 备注 | +|------|------|------| +| $\text{QoE}_k^{(s)}$ | 语义用户 $k$ 的体验质量 | $\text{QoE}_k^{(s)} = w_1 \cdot \text{SSim}_k + w_2 \cdot (1 - L_s/L_{\max})$ | +| $\text{QoE}_k^{(b)}$ | 传统用户 $k$ 的体验质量 | $\text{QoE}_k^{(b)} = \min(R_k^{(b)} / R_k^{\text{req}}, 1)$ | +| $\text{QoE}_{\text{sys}}$ | 系统整体QoE | $\text{QoE}_{\text{sys}} = \frac{1}{K} \sum_{k=1}^{K} \text{QoE}_k$ | +| $w_1, w_2$ | 语义QoE权重 | $w_1 + w_2 = 1$, 默认 $w_1 = 0.7, w_2 = 0.3$ | +| $Q_{\text{th}}$ | QoE切换阈值 | 默认 $Q_{\text{th}} = 0.6$ | + +## 博弈论与合作竞争 + +| 符号 | 含义 | 备注 | +|------|------|------| +| $\lambda(t) \in [0, 1]$ | 合作-竞争切换因子 | $\lambda(t) = \sigma(\beta \cdot (\text{QoE}_{\text{sys}}(t) - Q_{\text{th}}))$ | +| $\sigma(\cdot)$ | Sigmoid函数 | $\sigma(x) = 1/(1+e^{-x})$ | +| $\beta$ | 切换灵敏度参数 | 默认 $\beta = 5$ | +| $\mathcal{G} = \langle \mathcal{N}, \{\mathcal{A}_i\}, \{U_i\} \rangle$ | Stackelberg博弈 | | +| $\mathcal{N} = \{S, B\}$ | 玩家集合 | $S$: 语义智能体(Leader), $B$: 传统智能体(Follower) | +| $\mathcal{A}_i$ | 玩家 $i$ 的动作空间 | | +| $U_i$ | 玩家 $i$ 的效用函数 | | +| $U_S, U_B$ | 语义/传统智能体效用 | | +| $\mathbf{a}_S^*, \mathbf{a}_B^*$ | Stackelberg均衡策略 | | + +## MARL相关 + +| 符号 | 含义 | 备注 | +|------|------|------| +| $\mathbf{o}_i$ | 智能体 $i$ 的局部观测 | $\mathbf{o}_i = [h_{i,1}, \ldots, h_{i,N}, \overline{\text{QoE}}_i, \text{param}_i, N_i^{\text{alloc}}, \text{load}_i]$ | +| $\mathbf{a}_i$ | 智能体 $i$ 的动作 | $\mathbf{a}_i = [n_{\text{sub}}, p_{\text{frac}}, m_{\text{param}}] \in [0,1]^3$ | +| $\pi_i(\mathbf{a}_i | \mathbf{o}_i; \theta_i^{\mu})$ | 智能体 $i$ 的策略 (Actor) | 参数 $\theta_i^{\mu}$ | +| $Q_i(\mathbf{o}_1, \mathbf{o}_2, \mathbf{a}_1, \mathbf{a}_2; \theta_i^Q)$ | 智能体 $i$ 的Q函数 (Critic) | 参数 $\theta_i^Q$, CTDE架构 | +| $r_i^{\text{coop}}$ | 合作奖励 | $r_i^{\text{coop}} = 0.5 r_i^{\text{self}} + 0.3 r_j^{\text{self}} + 0.2 r^{\text{sys}}$ | +| $r_i^{\text{comp}}$ | 竞争奖励 | $r_i^{\text{comp}} = 0.8 r_i^{\text{self}} + 0.2 r^{\text{sys}}$ | +| $r_i$ | 混合奖励 | $r_i = \lambda \cdot r_i^{\text{coop}} + (1-\lambda) \cdot r_i^{\text{comp}}$ | +| $\gamma$ | 折扣因子 | 默认 $\gamma = 0.95$ | +| $\tau$ | 目标网络软更新率 | 默认 $\tau = 0.01$ | +| $\mathcal{D}$ | 经验回放缓冲区 | 容量 $|\mathcal{D}| = 10^5$ | + +## 定理相关 + +| 符号 | 含义 | 备注 | +|------|------|------| +| $U^*_{\text{co}}$ | 合作竞争均衡效用 | Theorem 1 | +| $U^*_{\text{coop}}$ | 纯合作最优效用 | Theorem 2 基准 | +| $U^*_{\text{comp}}$ | 纯竞争最优效用 | Theorem 2 基准 | +| $\mathcal{S}_c$ | 合作主导状态集 | $\{s : U_{\text{coop}}(s) > U_{\text{comp}}(s)\}$ | +| $\mathcal{S}_d$ | 竞争主导状态集 | $\{s : U_{\text{comp}}(s) \geq U_{\text{coop}}(s)\}$ | +| $\delta_{\min}$ | 最小模式优势差 | Theorem 2 | +| $L$ | QoE关于 $\lambda$ 的Lipschitz常数 | Theorem 3, $\beta L / 4 < 1$ | +| $\epsilon_Q$ | Q函数逼近误差 | Theorem 4 | +| $T$ | 训练迭代次数 | Theorem 4 | diff --git a/paper1.txt b/paper1.txt new file mode 100644 index 0000000..6162f7a --- /dev/null +++ b/paper1.txt @@ -0,0 +1,416 @@ +Deep Reinforcement Learning-based Resource +Allocation and Mode Selection for Semantic +Communication +Hyeonho Noh∗, Sojeong Park†, and Hyun Jong Yang∗ +∗Department of Electrical and Computer Engineering, Seoul National University, Korea +†Department of Electrical Engineering, Pohang University of Science and Technology, Korea +Abstract—In this paper, we aim to solve the joint resource extracts, compresses, and transmits features relevant to the +allocation and mode selection problem, in which an agent intended task from data, rather than transmitting the raw data +adaptivelyallocatescommunicationuserstoappropriateresource +itself. Thus, semantic communication employs lossy data +units and toggles between bit and semantic transmission modes +compression, but it excels in the realm of task performance +while determining the count of transmitted semantic symbols +in semantic communication mode. Specifically, in contrast to efficiency [11]. +the common yet unrealistic assumptions of prior research, In the field of text transmission, semantic communication +which posits the possibility of limitless data transmission models like DeepSC [11] have demonstrated excellent +over infinite periods, our focus shifts towards the realities of performance. However, they maintain a fixed transmission +unsaturated traffic conditions, where users transmit a finite +symbol size regardless of channel state information (CSI), +amount of data within restricted time frames. In order to +analogous to keeping the coding rate and modulation fixed +evaluate the efficiency of data transmission within the semantic +domain under unsaturated traffic conditions, we propose a in conventional communication. To take into account the +short-term semantic transmission rate (SR), as an evaluation benefitsofchanneldiversity,aresourceallocation(RA)model +metric of the joint problem. Under these unsaturated traffic that combines channel assignment and transmission volume +scenarios, the challenge emerges from the need to address a +control of semantic symbols was proposed [12]. Specifically, +combinatorialissue,optimizingresourceallocation,transmission +they defined the spectral efficiency in the realm of semantic +mode selection, and symbol lengths simultaneously across the +time-frequency axis. This task is compounded by the high communicationwhentransmittinginfinitesentencesoververy +degree of complexity and a significant number of unknown long transmission times [12]–[14]. However, this assumption +variables, making it a formidable challenge for conventional does not align with real-world scenarios, where user traffic +optimization techniques to solve effectively. In response, we +tends to be unsaturated, meaning that transmission time and +propose a deep reinforcement learning-based method that in +packet lengths are bounded by strict limitations [15]. +each time step allocates users to each resource units, determines +the communication transmission mode, and selects data size This paper goes beyond by addressing the joint RA and +according to communication environment and users’ packet mode selection (MS) problem in unsaturated traffic scenarios, +states.Extensiveexperimentsdemonstratesuperiorperformance whereUEsparticipateinuplinkcommunicationwhileholding +over conventional schemes in terms of semantic transmission +data of different sizes and numbers. The main contributions +performance. +are as follows: +Index Terms—Semantic communication, Resource allocation, +Deep reinforcement learning, Semantic rate, Mode selection • Building on the definition of semantic spectral efficiency in +a long-term perspective, we propose a short-term semantic +I. INTRODUCTION transmission rate (SR) to evaluate the data transmission +In beyond 5G and 6G, wireless communication demands rate in unsaturated traffic conditions. The SR reflects more +serving much more user equipments (UEs) with larger realistic communication scenarios, where the the frame +amounts of data, resulting in the challenge of a shortage in length is strictly limited the length of data varies. +the frequency spectrum [1], [2]. However, traditional wireless • Under the definition of SR, the performance superiority +communicationhasbeenprimarilyfocusedonthetransmission between bit communication and semantic communication +andreceptionofdatawithoutcomprehendingitsactualcontent changes depending on various signal-to-noise ratios (SNRs) +[3],[4].Asaresult,theamountofdatathatcanbetransmitted and data sizes. Therefore, we propose a joint RA +is strictly limited by the frequency spectrum in use. and MS problem that dynamically allocates UEs into +To address the frequency spectrum shortage problem resource units (RUs) in the frequency domain, adaptively +in conventional communication, task-oriented semantic selects transmission mode between bit and semantic +communication, which can surpass the Shannon capacity in communication, and determines the number of transmitted +terms of performing specific tasks, has been proposed and is semantic symbols for semantic communication. +activelyunderresearch[3],[5]–[10].Semanticcommunication • To solve the proposed RA and MS optimization problem +ISBN 978-3-903176-65-2 © 2024 IFIP 1 +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:53:50 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +Fig.1. Theproposeddeepreinforcementlearning-basedRAandMSprotocol +whileconsideringbothUEs’SNRanddatasize,whichisan RUs. Constraint (1b) imposes the restriction by which each +intractable problem due to its combinatorial aspect [16], we user can only occupy at most one channel. +propose an algorithm based on deep reinforcement learning Let h ∈ C denote the uplink communication channel +n,k +(DRL), which has proven to be a powerful tool for solving between the BS and the k-th UE on the n-th RU. Then, +complex resource management problems in recent year [5], the SNR for the k-th UE on the RU n is given by Γ = +n,k +[17], [18]. P |h |2/σ2. where P is the transmit power of the k-th +n,k n,k n,k +As a case study, we evaluate the proposed DRL-based RA UE on the RU n, and σ2 is the noise variance. +and MS algorithm in the field of text transmission. Our +C. Text Transmission Performance +results demonstrate that the proposed DRL-based RA and +MS algorithm can achieve superior performance in terms Many researchers rely on the specific yet well-developed +of sentence similarity [11], [12], [19], [20] over various large language model, known as bi-directional encoder +conventionalschemessuchasDeepSCandbitcommunication. representations from transformers (BERT) [21], to measure +how accurate the semantic information is transmitted in text +II. SYSTEMMODELANDPROBLEMFORMULATION +transmissionfield[11],[12],[19],[20].Inthispaper,weadopt +A. Scenario the calculate sentence similarity [12], which is defined by +We consider a scenario in which a base station (BS) B(s)B(ˆs)T +communicates with K UEs. Given the CSI and sentences F(s,ˆs)= , (2) +∥B(s)∥∥B(ˆs)∥ +to transmit of the UEs, the BS allocates each UE to +N RUs while also selecting the optimal transmission where B(s) represents the output embedding vector using +mode, which could be either conventional bit or semantic the BERT model for a sentence s. We leverage a pre-trained +communication. Additionally, if the BS decides to serve BERT model to compute the sentence similarity. Note that +UE with semantic communication, it needs to determine fromthesimilaritydefinitionin(2),wehave0≤F(s,ˆs)≤1, +the number of transmitted semantic symbols. The primary with 1 indicating the highest similarity and 0 indicating no +objective of the RA and MS process is to maximize task- relationship between two sentences. +specific performance metrics within the predefined packet +D. Definition of Semantic Rate +length for all UEs. The RA and MS process is shown in Fig. +1. With the definition of sentence similarity, SR is proposed +in [12] for measuring the semantic information transmission +B. Wireless Communication Model +rate using BERT model. However, unlike the conventional +We define a n,k as a binary RU assignment variable such approach, which calculates the average value of SR over +that a n,k = 1 if the k-th UE is allocated on n-th RU, and infinite frame length when sending a large amount of data, in +a n,k =0otherwise.Then,wecanrepresenttheconstraintson real communication environments, each user transmits limited +the RA as follows: data of different sizes. Furthermore, all users must transmit +(cid:88) data within a predetermined frame length to synchronize the +a ≤1, ∀k ∈K (1a) +n,k uplink transmission. To address these practical issues, we +n∈N +(cid:88) newly define the SR in this paper. +a n,k ≤1, ∀n∈N (1b) Let D = {s = [w ,w ,...,w ]}Dk−1 +k j,k j,k,0 j,k,1 j,k,Lj,k−1 j=0 +k∈K denotethetextdatasetforthek-thUEwithsizeD ,wheres +k j,k +where N = {0,1,...,N −1} and K = {0,1,...,K −1}. isthej-thsentencewithlengthL andw isthel-thword +j,k j,k,l +Constraint (1a) indicates the unique user assignment along all ofthej-thsentenceofthek-thUE.Inaddition,onecandefine +2 +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:53:50 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +the amount of semantic information of s as I (suts). +j,k j,k +Each sentence is transmitted via either bit communication or +semanticcommunication,asshowninFig.1.Wedenotem +n,k +as the binary transmission mode variable of the k-th UE on +then-thRUsuchthatm =0representsbitcommunication +n,k +while m =1 means semantic communication. +n,k +In bit communication, the transmitter protects information +from impairments such as noise or distortion by performing +rate adaptation through source coding and channel +coding based on the current SNR Γ . In the case of +n,k +semantic communication, successful transmission of semantic +information is guaranteed by extracting semantic information +and compressing the sentence length to c according to the +n,k +SNR Γ through semantic encoding and channel encoding. +n,k +The encoded symbol stream then can be represented by +Fig.2. SemanticratetableaccordingtoSNRanddatasizec n,k. +(cid:40) +C (s;Γ ,m ), if m =0, +x= bc n,k n,k n,k (3) +C (s;Γ ,c ,m ,β), if m =1. +sc n,k n,k n,k n,k follows: +(cid:88) (cid:88) +where C sc includes channel encoding, semantic encoding, max Φ= a n,k ϕ(D k ;Γ n,k ,c n,k ,m n,k ), (6a) +while C includes channel encoding, source encoding, and a,c,m +bc n∈Nk∈K +modulation, β is the parameter set of semantic and channel +s.t. (1a),(1b) (6b) +encoder networks. If x is sent, the signal received at the (cid:88) +c L ≤L ,∀n∈N,∀k ∈K, (6c) +receiver will be y = hx+z, where z is the additive white n,k j,k frame +Gaussian noise (AWGN) that follows CN(0,σ2I). With the j∈Dk +received signal, the decoded sentence can be represented as (cid:88) Lˆ ≤L ,∀n∈N,∀k ∈K, (6d) +j,k frame +ˆs= (cid:40) C b − c 1(y;Γ n,k ,m n,k ), if m n,k =0, (4) c j n ∈ , D k k ∈N,∀n∈N,∀k ∈K, (6e) +C−1(y;Γ ,c ,m ,β), if m =1, +sc n,k n,k n,k n,k a ,m ∈{0,1},∀n∈N,∀k ∈K, (6f) +n,k n,k +where inverse operation for C means the reverse process of +where a, c, and m are the set of all variable a , c , and +n,k n,k +C. Finally, the SR (suts/s) on n-th RU for k-th UE is defined +m for n ∈ N and k ∈ K, respectively. Clearly, due to its +n,k +by +nonconcave aspect, it is intractable to solve the RA and MS +(cid:80)Dk−1WI +·F(s ,ˆs ) +optimization problem [16]. +ϕ(D ;Γ ,c ,m )= j=0 j,k j,k j,k , +k n,k n,k n,k L +frame +III. PROPOSEDDRL-BASEDRAOPTIMIZATION +(5) +A. Proposed DRL structure +where W is the bandwidth and L is the frame length. +frame We propose a DRL structure consisting of an agent, which +Note that the sentence similarity heavily depends on the +performs RA and MS, based on the SNR and the data size. If +design of C and C . In bit communication, the design of +sc bc the allocated UE decides to utilize semantic communication, +C sa b t c isfi is ed st t a h n a d t a (cid:80) rdi D ze k d −1 a L c ˆ cord ≤ ing L to SN w R he Γ re . Lˆ Then i , s i t t h m e u le s n t g b th e the dimension of channel encoder and decoder c n,k , i.e., the +j=0 j,k frame j,k number of symbols for each word is selected to maximize the +of C bc (s j,k ;Γ n,k ). In semantic communication, the optimal S-SR Φ in (6). We obtain the solution by precomputing the Φ +channel coding dimension with respect to SNR has not been forallpossiblec andorganizingtheresultsintoanSRtable, +n,k +thoroughly surveyed. Thus, we define the channel coding +as shown in Fig. 2. In the case where the agent chooses bit +dimension of semantic communication for the n-th RU for +communicationfordatatransmission,thesentenceisconveyed +the k-th UE as c . Then, semantic communication transmits +n,k using the conventional bit communication protocol. +eachwordbypackingitwithasizeofc .Wedeterminethis +n,k +valuetoregulatethenumberoftransmittedsemanticsymbols. B. Definitions of Parameters in DRL +Similar to the approach in bit communication, it is essential Here, we define the result of RA and MS, whether it’s +to satisfy the condition (cid:80)D j= k 0 −1c n,k L j,k ≤L frame for the k-th bit communication or semantic communication, as an action. +UE on the n-th RU. The BS selects actions corresponding to each RU index at +each time step based on the current state. Therefore, one can +E. Problem Formulation +set t ∈ N. Then, the state space, action space, and reward +From (1) and (5), we formulate the joint RA and MS functions of the agent are defined below. +optimization problem that maximizes sum of SR (S-SR) as State Space: The state includes the CSI and dataset to +3 +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:53:50 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +transmit of the UEs, which is defined as ˜s n,k = {Γ n,k ,D k }. TABLEI +Additionally,theinitialstateforallRUsandallUEsisdefined THES-SRCOMPARISONOFTHEPROPOSEDANDCONVENTIONAL +as S = +(cid:83) (cid:83) +˜s . When the k-th UE is selected as +METHODSWITHRANDOMSNRANDRANDOMNUMBEROFSENTENCES. +0 n∈N k∈K n,k +an action during the DRL procedure, we set the Γ = −1 +n,k Random Random Max-SNR Max-SNR +for all n to mark it as an unavailable option. ++BC +SC +BC +SC +Action Space: The action is defined by a ∈ A, S-SR 1,776 2,464 2,169 2,498 +t +which represents the result of RA and MS on the t- +DRL DRL +th RU. Thus, we can represent the action as a t = +BC +SC Proposed +{(k,m )|a =1,∀k ∈K}. S-SR 2,374 3,091 3,113 +t,k t,k +Reward Function: We define the reward function of the +(cid:80) +agents as r = a ϕ . +t k∈K t,k t,k +coding dimension is fixed at eight and “Semantic” when the +C. DRL Training Process +channel coding dimension is optimized according to SNR. +Initialization: We introduce the Deep Q-network (DQN) +In the bit communication-based system, we adopt Huffman +[22] as the learning framework of the agent. Thus, we utilize +coding as a source coding and low-density parity check +a parameter θ that defines an action-value function Q(S,a;θ) +(LDPC) as a channel coding. We follow the 5G standard +for the agent. In addition, we initialize replay memories E for +in terms of coding rate and modulation and [26] to get +the agent to capacity E. +modulation and coding scheme index according to SNR. +Experience collection: At each time step t, the agent +We set the bandwidth W =180 kHz and the frame length +iteratively collects experience by selecting the actions. Each +L = 1024. We assume that the amounts of semantic +frame +actionisdrawninanepsilon-greedyfashionwithlineardecay, +information of all sentence are equivalent, i.e., I = 1, for +j,k +i.e., ϵ(e) = max{1−e/Z,0.01}, where Z is the decaying +all (j,k). In all experiments, the number of users is set to 5, +rate constant, and e is the episode step. The agent first selects +and the number of resource blocks is fixed at 5 3. +a random action a with probability ϵ(e) or selects a = +t t +argmax Q(S ,a;θ), otherwise. The agent stores transition B. Result Analysis +a t +at each time-step (S ,a ,r ,S ) in E. We first conduct a comparative analysis between the +t t t t+1 +Updating model parameters: With the stored experiences in conventional and proposed schemes in a scenario involving +the replay memories, the agent updates learning parameters, randomly varying data sizes ranging from 1 to 10 and SNR +θ. In the case of θ, the agent samples random mini- levels distributed uniformly between 3 dB and 15 dB, which +batch of B transitions (S ,a ,r ,S ) from E. We set is presented in Table I. From the result, we conclude that the +j j j j+1 +y = r if S is a terminal state or y = r + proposed DRL-based method achieves the highest S-SR over +j j j+1 j j +γmax Q(S ,a;θ), otherwise. Then, we get the training all conventional methods. +a j+1 +loss J(θ)= (cid:80) (y −Q(S ,a ;θ))2/B. The agent performs In the following, we assess the S-SR of the bit +j j j j +a gradient descent step on J(θ) and updates θ. communication only, semantic communication only, and +proposed schemes with the DRL method across different +IV. SIMULATIONRESULTS +number of sentences, as shown in Fig 3, to ascertain the +ToevaluatetheperformanceoftheproposedDRL-basedRA influenceofMS.WhenUEsendsarelativelysmallnumberof +andMSalgorithmunderscenariowherebothsemanticandbit sentences, it can achieve higher S-SR with bit communication +communication are available, we have conducted simulations becauseitcanreliablysendwithintheframelength.However, +with the proposed DRL algorithm and baseline methods. when sending a large number of sentences, compressing +sentences into semantic information and transmitting them +A. Experimental Setup +proves to be much more effective. Thus, the proposed method +We adopt the datasets named European parliament that allows users to flexibly choose between two modes of bit +proceedings parallel Corpus [23]. It includes around 2.0 and semantic communication based on the data size achieves +million sentences and 53 million words. We sample 200,000 the highest S-SR compared to the other two communication +sentence from the datasets and divides them into a training techniques. +dataset and a test dataset. In addition, we collect the sentence Fig. 4 shows the S-SR of the proposed and conventional +with the length of 4 to 30. methods along with different SNRs. In a low SNR +We examine baselines in RA methods and communication environment, the S-SR of bit communication deteriorates due +types. In RA methods, we investigate two methods: random to the failure of complete restoration of data. In contrast, +and max-SNR [24], [25]. The random method chooses UEs semantic communication provides a significantly better S-SR +regardlessofSNRanddatasizewhilethemax-SNRprioritizes in low SNR conditions; however, it shows a slightly lower S- +UEs based sorely on SNR. In terms of communication types, SR compared to bit communication when the SNR exceeds +semantic communication-based and bit communication-based or equals 9 dB. While semantic communication experiences +systemsareconsidered.Inthesemanticcommunication-based some loss in S-SR performance due to lossy compression, +system, we refer to it as “DeepSC” [11] when the channel bit communication achieves better performance in high SNR +4 +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:53:50 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +Korea government (MSIT) (No. RS-2023-00250191), and in +part by the New Faculty Startup Fund from Seoul National +University. +REFERENCES +[1] DenizGu¨ndz¨ etal., “Beyondtransmittingbits:Context,semantics,and +task-oriented communications,” IEEE J. Sel. Areas Commun., vol. 41, +no.1,pp.5–41,2023. +[2] Yalin E. Sagduyu, Sennur Ulukus, and Aylin Yener, “Task-oriented +communications for nextG: End-to-end deep learning and ai security +aspects,” IEEEWirelessCommun.,vol.30,no.3,pp.52–60,2023. +[3] Wanting Yang et al., “Semantic communications for future internet: +Fig. 3. The S-SR comparison of the proposed and conventional methods Fundamentals,applications,andchallenges,” IEEECommun.Surv.Tut., +with respect to the number of sentences. AWGN channel with a uniform vol.25,no.1,pp.213–250,2023. +distributionofSNRfrom3dBto15dBisconsidered. 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TheS-SRcomparisonoftheproposedandconventionalmethodswith +Commun.Magazine,vol.59,no.8,pp.44–50,2021. +respecttoSNR.ThenumberofsentencesallUEposesistwo. +[10] Xuewen Luo, Hsiao-Hwa Chen, and Qing Guo, “Semantic +communications:Overview,openissues,andfutureresearchdirections,” +IEEEWirelessCommun.,vol.29,no.1,pp.210–219,2022. +[11] Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li, and Biing-Hwang Juang, +environments due to its precise data reconstruction. However, +“Deeplearningenabledsemanticcommunicationsystems,”IEEETrans. +the proposed method outperforms all baseline methods across SignalProcess.,vol.69,pp.2663–2675,2021. +the entire SNR range by adaptively selecting the optimal [12] Lei Yan, Zhijin Qin, Rui Zhang, Yongzhao Li, and Geoffrey Ye Li, +“Resourceallocationfortextsemanticcommunications,”IEEEWireless +transmission mode. +Commun.Lett.,vol.11,no.7,pp.1394–1398,2022. +[13] XidongMuetal., “Heterogeneoussemanticandbitcommunications:A +V. CONCLUSION +semi-noma scheme,” IEEE J. Sel. 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Restrictions apply. \ No newline at end of file diff --git a/paper2.txt b/paper2.txt new file mode 100644 index 0000000..22b9cc5 --- /dev/null +++ b/paper2.txt @@ -0,0 +1,1511 @@ +2478 IEEEJOURNALONSELECTEDAREASINCOMMUNICATIONS,VOL.43,NO.7,JULY2025 +Hybrid Digital-Analog Semantic Communications +Huiqiang Xie , Member, IEEE, Zhijin Qin , Senior Member, IEEE, Zhu Han , Fellow, IEEE, +and Khaled B. Letaief , Fellow, IEEE +Abstract—Digital and analog semantic communications (Sem- andubiquitousconnectedintelligence.Tomeetthesedemands, +Com) face inherent limitations such as data security concerns target key performance indicators [2] have been proposed, +in analog SemCom, as well as leveling-off and cliff-edge effects aiming to ensure the seamless integration of these advanced +in digital SemCom. In order to overcome these challenges, +technologies in the next generation of mobile communication +we propose a novel SemCom framework and a corresponding +systems, e.g., 107 devices/km2 for connectivity, 60 b/s/Hz +system called HDA-DeepSC, which leverages a hybrid digital- +analog approach for multimedia transmission. This is achieved for spectral efficiency, and 100 us for end-to-end latency. +through the introduction of analog-digital allocation and fusion To materialize this vision, semantic communications [3] have +modules. To strike a balance between data rate and distortion, been envisioned as one of the potential technologies due to +wedesignnewlossfunctionsthattakeintoaccountlong-distance the low semantic errors, high spectral efficiency, and high +dependencies in the semantic distortion constraint, essential +transmission rates. By exchanging semantic information at +information recovery in the channel distortion constraint, and +both ends, semantic communications can reconstruct sources +optimal bit stream generation in the rate constraint. Addi- +tionally, we propose denoising diffusion-based signal detection or directly perform tasks with the tolerance of transmission +techniques, which involve carefully designed variance schedules errors. According to the communication paradigm, seman- +and sampling algorithms to refine transmitted signals. Through tic communications can be categorized into two categories: +extensivenumericalexperiments,wewilldemonstratethatHDA- analog semantic communications and digital semantic com- +DeepSC exhibits robustness to channel variations and is capable +munications. +of supporting various communication scenarios. Our proposed +Analog semantic communications [4], [5], [6], [7], [8], +framework outperforms existing benchmarks in terms of peak +signal-to-noise ratio and multi-scale structural similarity, show- [9], [10], [11], [12] convey the semantic information using +casing its superiority in semantic communication quality. continuous signals, which takes advantage of deep learning +Index Terms—Semantic communications, multimedia trans- (DL) to design end-to-end systems and maps the source to +mission,analogcommunications,digitalcommunications,hybrid the non-fixed-size constellations directly. There exist many +digital-analog communications. works for different modal data transmission. Xie et al. [4] +have developed a DL based semantic communication system, +I. INTRODUCTION +named DeepSC, for text transmission, in which the sentences +AS MOBILE communication systems transition from the +are mapped to the embedding vectors and then transformed +fifth generation (5G) to the sixth generation (6G), there +to the learned non-fixed-size constellation points. Yi et al. +is a need to address the evolving requirements of seamlessly +[5] introduced the explicit knowledge base to the DeepSC as +integrating virtual/augmented reality, remote control robots, +the side information and integrated the knowledge base into +the end-to-end optimization, achieving the higher bilingual +Received 15 May 2024; revised 16 December 2024; accepted 15 January +2025. Date of publication 10 April 2025; date of current version 19 June evaluationunderstudy(BLEU)scoreatthelowsignal-to-noise +2025. This work was supported in part by the National Key Research and ratio (SNR) regions. Weng et al. [6] have proposed an end-to- +Development Program of China under Grant 2023YFB2904300; in part by +end semantic communication system for speech recognition +the National Natural Science Foundation of China (NSFC) under Grant +62401227 and Grant 62293484; in part by Guangzhou Municipal Science and speech synthesis tasks, named DeepSC-ST. The speech +andTechnologyProjectunderGrant2025A04J3380;inpartbyFundamental signals are processed by the DeepSC-ST and output the con- +Research Funds for the Central Universities under Grant 21624349; in part +tinuous constellation points at the transmitter. Grassucci et al. +by the Hong Kong Research Grants Council under the Areas of Excellence +[7]havedesignedagenerativeaudiosemanticcommunication +Scheme under Grant AoE/E-601/22-R; in part by NSF ECCS-2302469, +Toyota;inpartbyAmazon;andinpartbytheJapanScienceandTechnology framework,whichtransmitsthecontinuousembeddingvectors +Agency (JST) Adopting Sustainable Partnerships for Innovative Research togeneratetheaudiosatthereceiver.Daietal.[9]haveinves- +Ecosystem (ASPIRE) under Grant JPMJAP2326. An earlier version of this +tigated the end-to-end image transmission problem, in which +paper was presented in part at the IEEE Globecom Workshop 2024 [1]. +(Correspondingauthor:ZhijinQin.) the image is non-linearly transformed into continuous signals +Huiqiang Xie is with the College of Information Science and with different lengths. Wu et al. [11] have investigated the +Technology, Jinan University, Guangzhou 510632, China (e-mail: +end-to-end image transmission for multiple-inputs multiple- +huiqiangxie@jnu.edu.cn). +Zhijin Qin is with the Department of Electronic Engineering, Tsinghua outputs(MIMO)channels.Similarly,theimagesareconverted +University, Beijing 100084, China, also with the State Key Laboratory of into continuous semantic features and adaptively assigned to +Space Network and Communications, Beijing 100084, China, and also with +different subchannels based on the channel state information +Beijing National Research Center for Information Science and Technology, +Beijing100084,China(e-mail:qinzhijin@tsinghua.edu.cn). (CSI). Wang et al. [12] have proposed a video semantic +ZhuHaniswiththeDepartmentofElectricalandComputerEngineering, communication system, in which the semantic features of +UniversityofHouston,Houston,TX77004USA,andalsowiththeDepart- frames are extracted into continuous signals and transmitted +ment of Computer Science and Engineering, Kyung Hee University, Seoul +using analog communication methods. +446-701,SouthKorea(e-mail:hanzhu22@gmail.com). +Khaled B. Letaief is with the Department of Electronic and Computer The continuous signals in analog semantic communications +Engineering, The Hong Kong University of Science and Technology, Hong have two benefits. One is to allow gradient propagation and +Kong,China(e-mail:eekhaled@ust.hk). +enable end-to-end optimization. The other is that the contin- +DigitalObjectIdentifier10.1109/JSAC.2025.3559149 +©2025TheAuthors.ThisworkislicensedunderaCreativeCommonsAttribution4.0License. +Formoreinformation,seehttps://creativecommons.org/licenses/by/4.0/ + +---PAGE BREAK--- + +XIEetal.:HYBRIDDIGITAL-ANALOGSEMANTICCOMMUNICATIONS 2479 +uous signals have a high degree of freedom that provides the Q1: Howtoenhancedatasecurityandalleviatetheleveling- +smoothness performance optimization varying from channel off and cliff-edge effects? +conditions,enablingbetterrobustnessinthelowSNRregimes. Q2: How it be compatible with purely analog and digital +However, continuous signals also have flaws. The commer- semantic communication? +cial encryption algorithms are designed for discrete signals, Q3: How to support the various communication environ- +e.g., bit streams, raising concerns about the data security of ments, e.g., the wide bandwidth scenario or the weak +continuous signal-based systems. Besides, in some scenarios communication scenario? +that require accurate transmission at the bit level, analog The concept of hybrid digital-analog (HDA) joint source- +semantic communications cannot meet the requirement due to channel codes [22] was proposed by Mittal et al. in 2002, +theapproximatelyinfinitecandidatesetsincontinuoussignals. which proves that HDA codes are capable of theoretically +Therefore,digitalsemanticcommunicationshaveattractedthe achievingtheShannonlimit(theoreticallyoptimumdistortion) +attention of researchers. and a less severe leveling-off and cliff-edge effects. Since +Digitalsemanticcommunications[13],[14],[15],[16],[17], then, the HDA codes have attracted much attention from +[18],[19],[20],[21]transmitsemanticinformationinthetype academicsandindustries[23],[24],[25],[26],[27].Skoglund +of discrete signal, which maps the source to bit streams or et al. [24] have proposed HDA codes for the bandwidth +fixed-size constellations. Tung et al. [13] have proposed the compression scenarios, and Ko¨ken et al. [26] have analyzed +quantizedjointsource-channelcodingforimagetransmission, therobustnessofHDAcodeswithbandwidthmismatch.HDA +namedDeepJSCC-Q,bymappingthecontinuoussignalstothe transmission is also adopted in the Japanese and Canadian +close points in the fixed-size constellations to be compatible television signal transmission [28], where video and speech +with some protocols. Similarly, Bo et al. [14] improved the signals are transmitted by analog and digital transceivers, +quantized joint source-channel coding by learning transition respectively. Yu et al. [29] have designed the HDA joint +probabilityfromsourcedatatodiscreteconstellationsymbols, source-channel coding for scalable video transmission, named +inwhichtheGumbel-Maxsamplingisemployedtosamplethe WSVC. which takes the 2D discrete wavelet transform for +constellation points from the learned transition probability so analog transmission and H.264/AVC for digital transmission. +that avoiding the non-differentiable quantization. Guo et al. Lan et al. [30] have formulated the video transmission distor- +[16] quantized the semantic information with the learnable tionsfirstandthenproposedasub-optimalresourceallocation +non-linear scalar quantizer, which learns to adopt dynamic scheme, which allocates the power and quantization bits. Tan +quantizationlevelsfordifferentsemanticvalues.Fuetal.[18] etal.[31]haveproposedtheoptimalresourceallocationforthe +have proposed the vector quantized semantic communication Internet-of-things (IoT) scenario. Three factors are optimized +system, in which the semantic vectors are quantized into toenhancethequalityoftherecoveredimage,includingdigital +bit streams with the learnable vector quantizer and trans- bandwidth,orthogonalpower,andnonorthogonalpowerofthe +mitted with the digital channel codings and modulations. analog signal. Yahampath [32] has considered the imperfect +Gao et al. [20] have developed an adaptive modulation and channel state information (CSI) for the video transmission, in +retransmission scheme by deriving the relationship between which the digital power is allocated by considering the CSI +bit-error-rate and the task performance, in which the seman- errors, and the remaining power is used to transmit superim- +tic information is quantized into fixed-length bit streams. posed analog QAM symbols. However, these works rely on +Huang et al. [21] have proposed an iterative training algo- linear transforms and ignore the semantic information behind +rithm for digital semantic communications, in which the deep data,whichisunsuitablefornon-linearsemantictransmission. +source codec are trained according to the chosen channel Inspired by the concept of HDA codes, we propose a novel +coding rate. framework called DL-based HDA semantic communication. +The above works on digital semantic communication This framework integrates the strengths of both analog and +achieve accurate transmission at the bit or symbol level and digital semantic communications to effectively tackle the +part of the works can apply the encryption algorithms to challenges mentioned earlier. Firstly, the HDA semantic com- +encrypt the bit streams. However, digital semantic commu- municationsystemscanimprovedatasecurityandalleviatethe +nication systems introduce unavoidable quantization errors leveling-off and cliff-edge effects by transmitting part infor- +due to the process of quantizing continuous signals to dis- mation with the continuous signals in analog communications +crete signals, which introduces the leveling-off effect. That is, (Q1). Besides, analog and digital semantic communications +the quality of the decoded source signal is limited because are special cases of HDA semantic communications. By con- +of the quantization errors. Besides, digital semantic com- trolling the ratio between analog and digital components, the +munications experience the cliff-edge effect varying from HDA semantic communications not only can be transformed +different channel conditions, which usually results in a drastic into purely analog or digital semantic communications (Q2) +degradation in performance at lower SNRs. Therefore, it is but also support the different communication scenarios (Q3). +imperative to adopt a new semantic communication paradigm The main contributions are summarized as follows: +that can address the limitations of both analog and digi- +A novel HDA semantic communication framework is +tal semantic communications. This paradigm should enhance • +proposed, which takes advantage of analog and digital +data security and mitigate the leveling-off and cliff-edge +semantic communications and addresses the limitations +effects. However, designing such a semantic communication +inherent in each. +system poses several challenges that need to be overcome, +Based on the HDA semantic communication framework, +namely, • +we propose an HDA semantic communication system, + +---PAGE BREAK--- + +2480 IEEEJOURNALONSELECTEDAREASINCOMMUNICATIONS,VOL.43,NO.7,JULY2025 +Fig.1. Theproposedhybriddigital-analogsemanticcommunicationframework. +named HDA-DeepSC, for multimedia transmission, in Given an image, I R3 H W, where H and W are the +× × +∈ +which the new analog-digital allocation and fusion mod- height and width of the image. The semantic information can +ules are proposed to generate the analog and digital be extracted by +components.Besides,thenewlossfunctionsaredesigned z= (I;α ), (1) +t +S +tocapturethelocalandglobalinformation,alleviatingthe +where z RM 1 is the semantic information and (;α ) is +distortions from channels, and balancing the source rate. ∈ × S · t +denotedasthesemanticencoderwiththeparameterα .Then, +To further improve the quality of the recovered images, t +• z is split into two parts with analog-digital allocation module +we proposed a diffusion-based framework enhanced sig- +by +nal detection by designing the variance schedule and +[z ,z ]= (z;θ ), (2) +sampling algorithm. A D A t +Based on extensive simulation results, the proposed wherez andz arethesemanticinformationtransmittedby +• A D +HDA-DeepSC outperforms the conventional and DL- the analog transmitter and the digital transmitter, respectively. +based communication systems and improves the system (;θ ) is analog-digital allocation with parameters θ . +t t +A · +robustness at the low SNR regime. 1) Analog Transmitter: The encoded symbols for analog +The rest of this paper is organized as follows. The sys- semantic transmission are represented as +tem model is introduced in Section II. The HDA semantic +x = (z ;β ), (3) +transmission is proposed in Section III. Section IV details the A C A A t +proposed diffusion-based signal detection. Numerical results where x +A +CLA× 1 is the encoded complex symbols and +∈ +are presented in Section V to show the performance of (;β ) is denoted as the analog channel encoder with the +C A · t +the proposed frameworks. Finally, Section VI concludes this parameter β . +t +paper. 2) Digital Transmitter: The entropy coding and quantizer +Notation: Bold-font variables denote matrices or vectors. will be employed firstly to convert z into bit streams by +D +Cn m and Rn m represent complex and real matrices of size +× × +n m, respectively. (µ,σ2) means circularly-symmetric b= E ( Q (z D )), (4) +× CN +complex Gaussian distribution with mean µ and covariance where b is the bit streams, () and () are denoted as +σ2. (µ,σ2) means Gaussian distribution with mean µ and the quantizer and entropy enc Q od · er, resp E ec · tively. Then, b is +N +covariance σ2. (a,b) means continuous uniform distribution encodedwithdigitalchannelencoders(e.g.,LDPCcodes)and +U +between a and b. () ∗ denotes the conjugate operation. x[k] fixed-size constellations (e.g., 16-QAM) by +· +represents the k-th element in the vector. +x = ( (b)), (5) +D D +M C +II. SYSTEMMODEL wherex D CLD× 1 istheencodedsymbols, ()represents +∈ M · +the fixed-size modulation, and () is denoted as the digital +D +AsshowninFig.1,weconsiderasingle-inputsingle-output C · +channel encoder. +(SISO)communicationsystem,whichaimstosendmultimedia +With the analog and digital symbols, the transmitted sym- +overtheair.TheproposedHDASemComframeworkconsists bols are x = [x ,x ] CL 1, where L = L +L . The +A D × A D +of the HDA transmitter, the wireless channel model, and the bandwidth compression ∈ ratio is defined as η = L . +HDA receiver, which employs both digital semantic transmis- 3 × H × W +sion and analog semantic transmission. +B. Wireless Channel Model +When x is transmitted over the block fading channels, the +A. The Hybrid Digital-Analog Transmitter +received signal can be given by +The HDA transmitter consists of a semantic encoder that +y =hx+n, (6) +extracts the semantic information behind images, analog- +digital allocation that allocates the semantic information for wherehisthechannelcoefficientthatremainsconstantwithin +analog and digital transmission, and channel encoders that a channel coherence time, n is the additive white Gaussian +protect the information over the air. noise(AWGN),inwhichn 0,σ2I .FortheRayleigh +∼CN n L +(cid:0) (cid:1) + +---PAGE BREAK--- + +XIEetal.:HYBRIDDIGITAL-ANALOGSEMANTICCOMMUNICATIONS 2481 +fadingchannel,thechannelcoefficientfollowsh (0,1); A. Model Design +∼CN +for the Rician fading channel, it follows h µ ,σ2 +∼CN h h TheproposedHDA-DeepSCisshowninFig.2.Thedesign +with µ h = r/(r+1) and σ h = 1/(r+1), where (cid:0) r is th (cid:1) e of each module is detailed below. +Rician coefficient. The SNR is defined as E( x 2)/E( n 2). 1) Semantic Codec: The semantic encoder comprises a +(cid:112) (cid:112) (cid:107) (cid:107) (cid:107) (cid:107) +convolutional layer and a residual Swin Transformer block. +C. The Hybrid Digital-Analog Receiver The first convolutional layer projects the images into vector- +shaped tokens, which are used as inputs to the residual Swin +The receiver comprises signal detection that estimates the +Transformer block in a permutation-invariant manner. Then, +transmittedsymbols,aanalog-digitalfusionmodulethatfuses +the residual Swin Transformer block consists of several Swin +the digital and analog semantic information, channel decoders +Transformerlayersandaconvolutionlayer,inwhichtheSwin +that alleviate the distortions from the wireless channels, and a +Transformer layer [33] originates from the Transformer and +semantic decoder that recovers the images with the received +introduces the local attention and shifted window mechanism +semantic information. +to improve the visual semantic understanding. Besides, a con- +Withtheleastsquares(LS)signaldetection,thetransmitted +volutional layer with spatially invariant filters in the residual +symbols can be estimated by +block can enhance the translational equivariance. The residual +h ∗ h ∗ connection allows for aggregation of the shallow and deep +xˆ = y =x+ n, (7) +h2 h2 semantic features. +| | | | Similarly,thesemanticdecoderconsistsoftheresidualSwin +where xˆ = [xˆ ,xˆ ] represents the estimated symbols. We +A D Transformerblock,convolutionallayers,andpixelshuffle.The +assume that h is the perfect CSI. After the signal detection, +residualblockistoenhancethevisualsemanticunderstanding. +the semantic features are recovered by the analog and digital +The residual connection provides a short connection from +receivers, respectively. +the semantic encoder to the semantic decoder, allowing the +1) Analog Receiver: The semantic features transmitted by +processingofreconstructiontofusevaryinglevelsoffeatures. +analog communications are estimated by +The convolutional layers and pixel shuffle form the recon- +ˆz A = CA− 1(xˆ A ;β r ), (8) s +u +t +p +ru +sa +c +m +tio +p +n +les +m +t +o +h +d +e +u +f +l +e +e +a +, +tu +in +re +w +a +h +n +i +d +ch +pix +th +e +e +ls +s +h +u +u +b +f +- +fl +p +e +ix +r +e +e +l +al +c +lo +o +c +n +a +v +t +o +e +l +s +ut +t +i +h +o +e +n +f +a +e +l +a +l +t +a +u +y +re +e +s +r +where zˆ A is the estimated semantic features and CA− 1( · ;β r ) to reconstruct the transmitted images. +is denoted as the analog channel decoder with parameter β r . 2) Analog-Digital Allocation and Fusion: At the trans- +2) Digital Receiver: For digital semantic transmission, the mitter, the analog-digital allocation module transforms the +transmitted bit streams are recovered firstly by original semantic information into essential and auxiliary +bˆ= CD− 1 M − 1(xˆ D ) , (9) s p e la m y a s n a ti n c im in p fo o r r m tan at t io ro n l . e T in he bu e il s d s i e n n g tia th l e s i e m m a a g n e t s ic an i d nf t o h r e m o at t i h o e n r +where CD− 1( +· +) represents th(cid:0)e digital c(cid:1)hannel decoder and parts of semantic information work to improve the quality of +1() is denoted as the fixed-size demodulation. Then, the the image. The essential part includes the basic information +− +M · +semantic features transmitted with digital semantic transmis- about the image, e.g., the low-frequency information, and +sion are recovered by needs to be delivered accurately and cryptographically. Only +the essential part cannot be obtained, the image cannot be +ˆz D = − 1( − 1(bˆ)), (10) built. However, the nature of analog semantic transmission is +Q E +where 1() and 1() are denoted as the entropy decoder continuous signals and not compatible with discrete encryp- +− − +E · Q · tion algorithms. Therefore, the essential part is transmitted +and dequantizer, respectively. +With zˆ and zˆ , the semantic features are fused by accurately by digital communication systems, in which the +A D +data encryption methods (e.g., symmetric cryptography and +ˆz= − 1(ˆz A ,ˆz D ;θ r ), (11) asymmetric cryptography) can be applied to encrypt the bit +A +streams to guarantee the data security of the essential part. +wherezˆ istherecoveredsemanticinformationand 1(;θ ) +A − · r A hyper codec is proposed to extract the essential part of +isrepresentedastheanalog-digitalfusionmodulewithparam- +the original semantic information, which is given by +eters θ . +r +Finally, the transmitted image can be reconstructed by z = (z;θ ), (13) +D t +H +Iˆ = S − 1(ˆz;α r ), (12) where H (z;θ t ) is denoted as the hyper encoder. As shown +in Fig. 2, the hyper encoder employs two convolutional layers +where 1(;α )representsthesemanticdecoderwithparam- +S − · r to downsample the original semantic information, such that +eter α . +r enables a larger receptive field and extracts the essential +semantic information. +III. HYBRIDDIGITAL-ANALOGSEMANTICTRANSMISSION +Theauxiliaryparthelpsimprovethequalityoftherecovered +In this section, we design an HDA semantic communica- image,whichistransmittedbyanalogcommunicationsystems +tionsystem,namedHDA-DeepSC,forheterogeneouswireless withthefollowingbenefits.Analogcommunicationsystemsdo +communication environments. Then, we develop the new loss not have a cliff effect and are suitable for optimizing systems +function to train the HDA-DeepSC with the proposed training inanend-to-endmanner.Toextracttheauxiliarypart,wefirst +algorithm. analyze the entropy of z conditioned on z˜, H(z z˜), which +| + +---PAGE BREAK--- + +2482 IEEEJOURNALONSELECTEDAREASINCOMMUNICATIONS,VOL.43,NO.7,JULY2025 +Fig.2. Thestructureoftheproposedhybriddigital-analogsemanticcommunicationsystem. +qualifies the uncertainty about z when z˜ is known. In other The design of analog-digital allocation and fusion can also +words, it measures the remaining information of z when z˜ is be viewed as a coarse-to-fine processing. The digital and +known. The lower bound of H(z z˜) is derived by analog component transmits coarse and auxiliary semantic +| +information about the basics and supplements of the image, +H(z ˜z)=H(z,˜z) H(˜z) +| − respectively. The receiver fuses the coarse and auxiliary +H(z) H(˜z), (14) semantic information to obtain fine semantic information, +≥ − +which is used to recover the high-fidelity images. +where the equals hold when z˜ is close to z. z˜ = +3) Digital Transceiver: The quantizer module rounds ele- +1 1( (z ));θ is the recovered semantic infor- +H − Q − Q D r ments of z to the nearest integer, z˜ . Then, the arithmetic +mation based on essential part without consideration of D D +(cid:0) (cid:1) coding converts z˜ into bit streams, in which the arithmetic +transmission errors. 1(;θ ) is denoted as hyper decoder, D +− r +H · coding is one kind of entropy coding. The entropy coding +wheretwoconvolutionallayersareemployedtoupsampleand +requires the distribution of z˜ in advance. Similarly to [34], +recover the basic semantic information. D +we model z˜ using a non-parametric, fully factorized density +Byobserving(14),wecanobtaintheremaininginformation D +model by +of z when z˜ is known, i.e., the auxiliary part, by +1 1 +z A =z − ˜z, (15) p(˜z D | ψ)= p ˜zD[i] | ψ[i] ψ[i] ∗U −2 , 2 (˜z D [i]), +where z A is transmitted by analog communications. The (cid:89) i (cid:18) (cid:18) (cid:19)(cid:19) (17) +derivation is in Appendix A. +At the receiver, the analog-digital fusion module is where ψ[i] is the parameters of each univariate distribution +employed to obtain the fine semantic information by fusing p . Like most cases, we model the quantization errors +the essential and auxiliary parts, which is given by w z˜ i D th [i] t | h ψ e [i] uniform distribution. Therefore, we convolve each +ˆz= − 1(ˆz A ,ˆz D ;θ r )= − 1(ˆz D ;θ r )+ˆz A , (16) non-parametric density with a standard uniform density to +A H better match the prior of z˜ . +D +where 1(;θ ) shares the same weights with the hyper For digital channel codec and modulation, we adopt the +− r +H · +decoder in the transmitter. adaptive modulation and coding for different SNRs. + +---PAGE BREAK--- + +XIEetal.:HYBRIDDIGITAL-ANALOGSEMANTICCOMMUNICATIONS 2483 +4) Analog Transeiver: The analog channel codec aims to p(z)logp(z)dz +− +compress the semantic features and transmit them effectively +(cid:90) +over the air. Similarly to the previous works [35], the analog = p(z)p(ˆz z)logq(z ˆz ,θ )dzdˆz +H(z) +D D r D +channel codec mainly employs the fully connected layers | | +(cid:90) +to transmit the semantic information due to global semantic =E E [logq(z ˆz ,θ )]+H(z). +z +∼ +p(z) ˆzD∼ p(ˆzD| z) +| +D r +information preservation. Compared with the convolutional (20) +neural network (CNN) layer to capture the local information, +where the inequation follows KL[p(z zˆ ),q(z zˆ ,θ )] 0, +the dense layer is good at capturing global information and | D | D r ≥ +in which KL[, ] is the Kullback-Leibler (KL) divergence and +preserving the entire attributes, which follows the target of · · +q(z zˆ ,θ ) is the variational approximation of p(z zˆ ). +the analog channel codec. This can enhance the system’s | D r | D +For the sake of argument, assume for a moment that the +robustness to channel noise. +likelihood is given by +B. Loss Function Design q(z ˆz D ,θ r )= z,(2λ z ) − 1I , (21) +| N +The wireless multimedia transmission problem can be +(cid:16) (cid:17) +viewed as the classical rate-distortion optimization problem, where z = 1(ˆz ;θ ). The log-likelihood then works out +− D r +which includes distortion and rate constraints. H +to be the squared difference between z and z weighted by λ . +z +1) Loss Function Design for Distortion Constraints: The +Then, the I(z,ˆz ) can be rewritten as +D +distortion constraint can be categorized into semantic and +(cid:98) +channel distortion constraints. For semantic distortion con- I(z,ˆz ) λ E z z 2 +H(z)+constant. (22) +D z +straint, except for the pixel difference considered in most ≥− (cid:107) − (cid:107) +(cid:104) (cid:105) +works, we further introduce the frequency difference of the Submitting (22) into (19) and omitting the constant, the +images. The designed loss function for semantic distortion can be written as +CD +L +constraint is given by +E[ z ˆz ]+λ E z z 2 H(z). (23) +=E I Iˆ 2+λ (I) (Iˆ) , (18) L CD ≈ (cid:107) − (cid:107) z (cid:107) − (cid:107) − +SD +L (cid:107) − (cid:107) F|F −F | (cid:104) (cid:105) +(cid:104) (cid:105) If we freeze the semantic codec during training, H(z) can be +where λ is the weight and () represents the Fourier +F F · technically dropped out from CD . +transform. The first item in (18) refers to the pixel difference L +2) Loss Function Design for Rate Constraints: For rate +of the image, we assume that the pixels of the image follow +constraint, the analog transmitter designs the fixed-length +the Gaussian distribution without loss of generality and thus +output. Therefore, we consider the rate constraint for the +employ the mean-square error (MSE) loss. The second item +digital transmitter, which is given +in (18) refers to the frequency difference of the image, we +considerthelearningoflong-rangedependenciesoftheimage =E[ log(p(˜z ψ)))], (24) +Rate D +L − | +and design the Fourier-based loss function. In detail, we +map the images into the frequency domain and compare the where p(z˜ D ψ) is given in (17). By minimizing the rate +| +difference between the original and transmitted images. The constraint, we can optimize the distribution of z˜ D and reduce +reasons behind the design can be summarized as the number of bits generated by the arithmetic coding. +The MSE loss guides the neural networks to recover +• +the local pixels of the images by comparing the pixel C. Training Details +difference,whichignoresthelong-rangedependenciesof +The proposed training algorithm is shown in Algorithm 1. +the image. +We adopt three-stage training methods. The first stage is +The Fourier-based loss can help the neural network learn +• the long-range dependencies of the image. Because the to train the semantic codec with the L SD , which enables +effectivesemanticextraction.Afterthesemanticcodecfinishes +same frequency in the frequency domain refers to the +training, the second stage is to train the hybrid transceiver +different pixels at the different positions of the image. +with +λ , which aims to reduce the distortions +CD r Rate +For the channel distortion constraint, we consider the L L +from physical channels as well as the number of bit streams. +distortions from channels and the transmission of essential +We can drop out the H(z) in since we freeze the +CD +information. The designed loss function is given by L +semantic codec during training. The non-differentiable opera- +=E[ z ˆz ] I(z,ˆz ), (19) tions, e.g., the quantization, entropy coding, and modulation, +CD D +L (cid:107) − (cid:107) − +will block the gradient back-propagation from receiver to +where the first item minimizes the distortions from chan- +transmitter. Therefore, we substitute additive uniform noise +nels and the second item maximizes the mutual information +for the non-differentiable operations itself during training, +between z and zˆ to make zˆ contains more information +D D i.e., z˜ = z +u in line 10 of Algorithm 1. Besides, we +D D +of z. However, directly optimizing the I(z,zˆ ) is hard. We +D choose the error-free transmission for the z˜ due to two +D +derive the lower bound of I(z,zˆ ) by +D factors, one is that the number of generated bit streams is +p(z ˆz ) muchsmallerthantheconventionalsourcecoding,e.g.,JPEG; +I(z,ˆz )= p(z,ˆz )log | D dzdˆz +D D p(z) D another one is the accurate bit transmission characteristic of +(cid:90) +digital communication. Finally, we train the whole network +≥ p(z,ˆz D )logq(z | ˆz D ,θ r )dzdˆz D with L SD +λ r L Rate to improve the quality of the recovered +(cid:90) + +---PAGE BREAK--- + +2484 IEEEJOURNALONSELECTEDAREASINCOMMUNICATIONS,VOL.43,NO.7,JULY2025 +Algorithm 1 HDA-DeepSC Training Algorithm +1 1 +1 +1 +1 +1 +1 1 +1 1 +2 +2 +2 +2 +2 +2 +2 +2 +2 +2 +3 +3 +3 +3 +3 +1 +2 +3 +4 +5 +6 +7 +8 +9 +0 1 +2 +3 +4 +5 +6 7 +8 9 +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +0 +1 +2 +3 +4 +F +F +F +u +u +u +n +n +n +)( c e d o C ci t n a m e S ni a r T : n oi t c +I . t e s a t a d m o rf el p m a S : t u p nI +α ; I ( = z ) , t +S +ˆ 1 α ; z ( ) = I , − r S +e t u p m h ) 8 t o i 1 w C ( , D S +L (cid:127) α α , h ti w t n e c s e d t n ei d a r n G i a r T . D S t r L +1 α ; ) ( ) α ; ( d n a . : n r u t e R − t r S · · S )( r e vi e c s n a r T di r b y H ni a r T : n oi t c +m e S el p m a s d n a c e d o c ci t n a m e s e z e e r F : t u p nI +z . s e r u t a ef +: r e t ti m s n a r T +n oi t a c oll A l a ti gi D - g ol a n A / / +θ ; z ( = z ) , D t +H (cid:127) 1 1 ˜ , u u z = + z , , D D 2 2 U − 1 ˜ ˜ θ z ( ) ; = z , − D r +M̂ H ˜z z = z . A +− r e t ti m s n a r T l a ti gi D / / +˜z si d t n ei d a r g di o v a o t e t e i m rf - s r n o a r r r e T D +r e t ti m s n a r T g ol a n A / / +β z = x ( ) ; , A A A t C , n oi t a zil a m r o n r e w o P +x .ri a t e i h m t s r n e a v r o T A : r e vi e c e R +˜ y z e vi e h d c t ) e n i 6 w R a ( . D A +r e vi e c e R l a ti gi D / / +˜ ˆ z = z , D D +(cid:127) 1 ˆ = θ z z ( ) ; . − D r H +r e vi e c e R g ol a n A / / +ˆx y b n oi t c e t e d t l a e n g g ) o 7 i S t ( , A +1 ˆ ˆ z β x = ( ) ; . − A A r A C +n oi s u F l a ti gi D - g ol a n A / / +(cid:127) ˆ ˆ + z z = z . A +λ + e t u p m h ) ) d 4 3 t o n i 2 2 w C a ( ( . et D a C R r +L L(cid:127) +β β θ θ , , , h ti w t n e c s e d t n ei d a r n G i a r T t r t r +λ + . et D a C R r +L L +1 θ ; ( β β ; ; ( ) ( ) ) d n a , , , : n r u t e R − A t t r A · H C · · C +1 θ ; ( ) . +− r +· H +)( k r o w t e N el o h W ni a r T : n oi t c +I . t e s a t a d m o rf el p m a S : t u p nI +ˆI . t e g o t 3 d n a , 8 2 - 8 , 2 s e nil t a e p e R +λ + e t u p m h ) ) d 8 4 t o n i 1 2 w C a ( ( . et D a R S r +L L (cid:127) α β β α θ θ , , , , n e c s e d t n ei d a r n G i a r T , t t r r t r +λ + . +et D a R S r +L L +. C S p e e D - A D H e h T : n r u t e R +a +p a +t +n +p +w +ci t +a e +h ti +.r +Algorithm 2 HDA-DeepSC Inference Algorithm +image and reduce the number of bit streams in an end-to-end +manner, which converges to the global optimization. +When the whole network has been trained, we can employ +the model to transmit the image wirelessly. The inference +algorithm is presented in Algorithm 2. We remove the addi- +tive uniform noise and replace it with the non-differentiable +operations. +The three-stage training algorithm ensures that each stage +can converge to the local optimum and avoids the mismatch +of gradient descent. Besides, the approximate quantized noise +1 +1 +1 +1 1 +1 +1 +1 +1 +1 2 +2 2 +2 +2 +2 +2 +2 +1 +2 +3 +4 +5 +6 7 +8 +9 +0 +1 +2 +3 4 +5 +6 +7 +8 +9 0 +1 2 +3 +4 +5 +6 +7 +F u n )( e c n e r ef nI C S p e e D - A D H n oi t c +I . t e s a t a d m o rf el p m a S : t u p nI +: r e t ti m s n a r T +α ; I ( = z ) . t S +n oi t a c oll A l a ti gi D - g ol a n A / / +θ ; z ( = z ) , D t H +˜ z ( = ) z , D D Q 1 1 ˜ ˜ = z θ z ; ( ( ) ) , − − D r H Q˜z +z = z . A +− +r e t ti m s n a r T l a ti gi D / / +˜z ( = b ) , D E +( )) b = x ( . D D C M +r e t ti m s n a r T g ol a n A / / +β z = x ( ) ; , A A A t C , n oi t a zil a m r o N r e w o P +x [ = x x ] , ti t m r s e n v a o r T D A +: r e vi e c e R +y h ti w e vi e c ) e 6 R ( .y +b n oi t c e t e d t l a e n g g ) o 7 i S t ( +r e vi e c e R l a ti gi D / /ˆ 1 1 ˆ = b x ( ) , − − D D C M +ˆ 1 1 ˆ )) b = ( ( z , − − D M̂ Q E (cid:127) 1 ˆ = θ z z ( ) ; . − D r +H +r e vi e c e R g ol a n A / / +1 ˆ ˆ z β x = ( ) ; . − A A r A C +n oi s u F l a ti gi D - g ol a n A / / +(cid:127) ˆ ˆ z = z + z , A +ˆ 1 ˆ α = ; z ( ) I . − r +S ˆI . : n r u t e R +h +ˆx +e +A +: +.ri a +n a d ˆx D . +helps avoid the disappearing gradient, which enables end-to- +end training. Moreover, the inference algorithm indicates that +the digital component can adopt the encryption algorithm to +protect the digital bits and the adaptive modulation coding +against channel distortions. +IV. DIFFUSIONFRAMEWORKENHANCED +SIGNALDETECTION +This section provides an overview of the de-noising dif- +fusion framework and its background. Subsequently, we +introduce a novel diffusion-based signal detection method +called DiffSDNet. DiffSDNet is developed by incorporating +a carefully designed variance schedule into the training and +sampling algorithms. The diffusion-based de-noise module is +the optional part of the HDA-DeepSC, which can further +improve the robustness of the HDA-DeepSC. +A. De-Noising Diffusion Framework +Given a random noise as input, the denoising diffusion +framework [36] models the generative processing through +multiple de-noising steps. Each step iteratively enhances the +generative results by removing the predicted noise, akin to +Langevin dynamics. The de-noising diffusion framework is +divided into forward process and reverse process. + +---PAGE BREAK--- + +XIEetal.:HYBRIDDIGITAL-ANALOGSEMANTICCOMMUNICATIONS 2485 +1) Forward Process: The forward process is fixed to a B. The Proposed De-Noising Diffusion-Based Signal +MarkovchainwithT stepsthatgraduallyaddsGaussiannoise Detection +tothedataaccordingtoavariancescheduleγ , ,γ ,which +1 T The detected signals in (7) can be rewritten as +··· +is given by +xˆ =x+n˜, (30) +v(0) +→ +v(1) +→ +v(2) +→···→ +v(T − 1) +→ +v(T), (25) +where n˜ = +h∗ +n is an effective noise after the signal detec- +h2 +where v(0) is the input information, p v(t) v(t 1) = +tion.Weemp|lo|ytheblock-fadingchannelmodelin(6),where +− +the h keeps constant. Therefore, the n˜ follows a circularly +1 γ(t)v(t 1),γ(t)I , and p(v(T)) is modeled with +N − − (cid:0) (cid:12) (cid:12) (cid:1) symmetric complex Gaussian distribution with zero mean and +((cid:16)0 (cid:112) ,I).Duetothereparam(cid:17)eterizationofnormaldistribution, scaled variance, σ2 =σ2/h2. +v N (t) can be represented as Since the coeffi n˜ cients n o | f | p v(t) v(t 1) in (25) should +− +2 +v(t) = 1 γ(t)v(t 1)+ γ(t)(cid:15)(t) satisfy 1 γ(t) +γ(t) =(cid:0)1, we(cid:12) rewritt(cid:1)en xˆ as +− − − (cid:12) +=(cid:112)1 γ¯(t) 2 v(0)+(cid:112)γ¯(t)¯(cid:15)(t), (26) +(cid:16)(cid:112) +x˜ = +(cid:17) +1 x+ σ n˜ (cid:15), (31) +− √1+σ √1+σ +(cid:113) n˜ n˜ +(cid:0) (cid:1) +where ¯(cid:15)(t) (0,I) and γ¯(t) = 1 (1 γ(t)). where x˜ =xˆ/√1+σ and n˜ =σ (cid:15),(cid:15) (0,I). +∼ N − t=1 − n˜ n˜ ∼CN +Observe (26), the forward process recu(cid:113)rrently adds the Gaus- Comparing (31) with (26), we find that the wireless trans- +(cid:81) +sian noise step by step to make v(0) approach the normal mission is similar to the forward process. We model x and x˜ +distribution, which can be viewed as the encoding processing in (31) as v(0) and v(t) in (26). It is natural to employ the +without learnable parameters. reverseprocesstorefinex˜,suchthatobtainsthemoreaccurate +2) Reverse Process: The reverse process is also defined as x. Given the x˜ and σ n˜ , we adopt (27) to remove the noise +a Markov chain with T steps starting at v(T), which is given in x˜ to closer the x. However, the existing variance schedule +by of p v(t) v(t − 1) and sampling algorithm are unsuitable for +wireless communications. We need to design the variance +v(T) v(T − 1) v(T − 2) v(1) v(0), (27) sched (cid:0) ule a (cid:12) (cid:12)nd sam (cid:1) pling algorithm by considering the channel +→ → →···→ → +SNR. +where q v(t − 1) v(t) = µ v(t);ω ,σ(t)I . The reverse 1) Variance Schedule Design: A variance schedule refers +N +processg (cid:0) enerates (cid:12) the (cid:1) v(t − 1) b (cid:0) as (cid:0) edonv( (cid:1) t),inw (cid:1) hichthemean to the way in which the mean and variance of the added +of v(t − 1) is mo(cid:12)deled with neural network with the v(t) as noisechangesoverthecourseofthediffusionprocess.During +input. this process, the mean and variance of the added noise is +From (26), we can observe that v(t − 1) can be predicted adjustedateachstep,affectingtheamountofnoiseintroduced +with v(t) and v(0) by removing the added noise. Therefore, at each stage, therefore variance schedule determines how the +µ v(t);ω can be modeled as noise level evolves during the diffusion process. A variance +schedulecanimpactthequalityofgeneratedxandthemodel’s +(cid:0) µ(v(t) (cid:1) ;ω)= 1 v(t) γ(t) (cid:15)(v(t);ω) . (28) convergence behavior. +1 γ(t) − γ¯(t) The variance schedule should satisfy the γ¯(T) 0. Based +− (cid:18) (cid:19) ontheconstraint,wedesignthevarianceschedulew → ithT =50 +where (cid:15) v(t);ω (cid:112) predicts the noise added to v(t). From (28), steps, which is given by +thereverseprocesspredictstheGaussiannoiseateachstepand 0.5t +thenrem (cid:0) ovesthe (cid:1) predictednoisetorestorethev(0) fromv(T) γ(t) = , (32) +T +with learnable parameters, which can be viewed the decoding +processing. which γ¯(50) e − 6.375 1 . The designed variance schedule +≈ ≈ +The loss function for the diffusion-based model at step t is includes 50 different noise levels. The reasons behind the +defined as designed variance schedule can be summarized as +Compared with the conventional diffusion-based frame- +2 • +(t) =E ¯(cid:15)(t) (cid:15) 1 γ¯(t) 2 v(0)+γ¯(t)¯(cid:15)(t);ω,t . workwith1,000stepsforgenerativetasks,weempirically +LDiff (cid:34)(cid:13) − (cid:18)(cid:113) − (cid:19)(cid:13) (cid:35) find that the de-noise task does not need too many steps +(cid:13) (cid:0) (cid:1) (cid:13)(29) due to the low complexity of the de-noise task. +(cid:13) (cid:13) +(cid:13) (cid:13) Wedesignamonotonicfunctionofγ(t)toachievecoarse- +• +During training, we sample the t first and model the v(t) to-finede-noiseprocessing,whichhasanunequalinterval +with v(0) by adding the Gaussian noise with the scheduled SNR, e.g., a small interval in high SNR regions and a +variances. large interval in low SNR regions. The unequal interval +Compared with the previous de-noise frameworks, e.g., SNR can speed up the de-noise processing with fewer +DnCNN, that predict the noise with only one step, the steps at low SNR regions. +de-noising diffusion framework can predict the noise with 2) Sampling Algorithm: The sampling algorithm performs +multiple steps, such that matches the distributions of noise the reverse process by sampling the steps. For example, the +and achieves better performance of de-noise. Therefore, we conventionaldiffusion-basedframeworkusuallysamples1,000 +propose a de-noising diffusion-based signal detection method. steps from T 0 [36] or 100 steps with the subsequence +→ + +---PAGE BREAK--- + +2486 IEEEJOURNALONSELECTEDAREASINCOMMUNICATIONS,VOL.43,NO.7,JULY2025 +Algorithm 3 Dynamic Sampling Algorithm +1 +2 +3 +4 +5 +F u n a n y D n oi t c +e h T : t u p nI +˜x e zil ai ti nI +˜t b e h t d ni F +(cid:127) ˜t = t r of +) 1 t ( = v − +0( v : n r u t e R +)( g nil p m a S ci m +˜xl a n gi s d e t c e t e d +p g ni t r a t s e h t s a +σ y h ) 3 ti 3 w ( . n˜ +1 o d (cid:127) +1 ) t ( v √ − )t( γ 1 +− +) +o +:d +n a +v, t ni +)t( γ )t( γ¯ +σ n˜˜) +t ( +(cid:127) v ( +.. +( t ) ; ω ) +(cid:127) +of T 0 [37], in which v(T) is the first sampled step. +→ +However,startingfromv(T) isunsuitableforsignaldetection. +The detected signals will start from different v(t) where t +depends on the received SNR at the receiver. Therefore, we +proposeadynamicsamplingalgorithmshowninAlgorithm3. +Fig. 3. The PSNR performance comparison between Analog DeepSC and +Firstly, given the known σ n˜ , we search the starting point t˜ AnalogDeepSCwithdifferentdenoisersontheKodakdataset. +at the reverse process, which is given by +Low-density parity check (LDPC) coding and +1 [γ¯(t˜+1),γ¯(t˜)]. +(33) +• +capacity-achieved coding are used for the channel +√1+σ ∈ +n˜ coding. +Then, the signal detection aims to recover the transmitted The adaptive modulation and coding (AMC) is +• +signals as more accurate as possible. Therefore, we change employed for different SNRs, including 1/2 coding +the random sampling to deterministic sampling. In detail, ratewithBPSK,1/2codingratewithQPSK,3/4cod- +we reduce the degree of randomness in the reverse process ing rate with QPSK, 1/2 coding rate with 16QAM, +by setting σ(t) in (27) equals to zero, which means that and 3/4 coding rate with 16QAM. +the q v(t − 1) v(t) changes from µ v(t);ω ,σ(t)I to Analog semantic communication systems: The purely +deterministic µ v(t);ω . N • analogsemanticcommunicationofHDA-DeepSCtrained +(cid:0) (cid:12) (cid:1) (cid:0) (cid:0) (cid:1) (cid:1) +(cid:12) with MSE loss. +(cid:0) (cid:1) +V. NUMERICALRESULTS Digital semantic communication systems: The +• +In this section, we compare the proposed HDA-DeepSC DeepJSCC-Q proposed in [13]. +with DL-based semantic communication systems and digital ConventionalHDAtransmissionsystemswith2Ddiscrete +• +communication systems over AWGN and Rician fading chan- cosine transform and scaler quantization [30]. +nels, where we assume the perfect CSI for all schemes. Denoising convolutional neural network (DnCNN) as the +• +one-step de-noise benchmark +The LDPC codes we use are from the 802.11ad standard, +A. Implementation Details +with blocklength 672 bits for both the 1/2 and 3/4 rate codes. +1) The Dataset: We choose the DIK2K dataset [38] for +The coherent time is set as the transmission time for each +training, which contains 1,000 images with different scenes. +image in the simulation. We set r =1 for the Rician channels +The Kodak dataset is used for testing. +and h=1 for the AWGN channels. Peak signal-to-noise ratio +2) Training Settings: The semantic codec consists of 6 +(PSNR) and multi-scale structural similarity (MS-SSIM) are +Swin-Transformer layers, respectively. Each layer is with 6 +used as the metrics to measure the local and global quality of +heads and a width of 120. The diffusion-based model adopts +images. The unit of MS-SSIM is dB by +the structures of OpenAI-UNet. The λ , λ , and λ is 0.1, +z r +0.1, and 0.0005, respectively. The learn F ing rate is 2 × 10 − 4. MS − SSIM(dB)= − 10log 10 (MS − SSIM). (34) +ThedeviceforsimulationconsistsofIntelR XeonR Platinum +(cid:13) (cid:13) +8352V and the NVIDIA GeForce RTX 4090. The encryption B. Denoising Networks Comparisons +algorithm is AES encryption. +Fig. 3 presents the PSNR performance for the analog +3) Benchmarks and Performance Metrics: We adopt the +DeepSCwithdifferentdenoisers.Firstobservethattheanalog +separatesource-channelcoding,theDL-basedanalogsemantic +DeepSC with denoiser has a larger PSNR than that without +communication system, the DL-based digital semantic com- +denoiserinthelowSNRregimes.Thisvalidatestheeffective- +municationsystem,andtheone-stepdenoisingnetworkasthe +ness of the denoiser in reducing the noise level. For the small +benchmarks, which are detailed as follows. +noise level at the high SNR regimes, the analog DeepSC is +Separate source-channel coding: Employ the source and capableofrestoringthesignalsthereforeallmethodsachievea +• +channel coding separately to transmit the images, we use similar PSNR as the SNR increases. Furthermore, we observe +the following technologies, respectively: thattheanalogDeepSCwithDiffSDNetoutperformsthatwith +Better Portable Graphics (BPG) for image source DnCNN with 0.6dB in terms of PSNR. This suggests that the +• +coding, the state-of-the-art image compression multiple-step denoiser has a stronger power of denoising than +method. the one-step denoiser. + +---PAGE BREAK--- + +XIEetal.:HYBRIDDIGITAL-ANALOGSEMANTICCOMMUNICATIONS 2487 +Fig.4. ComparisonbetweenHDA-DeepSCandtheAnalogDeepSC,DeepJSCC-Q,andBPGwithdifferentchannelcodingontheKodakdatasetoverAWGN +channels. +Fig.5. ComparisonbetweenHDA-DeepSCandtheAnalogDeepSC,DeepJSCC-Q,andBPGwithdifferentchannelcodingontheKodakdatasetoverRician +channels. +TABLEI channels with a 1/6 bandwidth compression ratio. For AWGN +THEPSNRCOMPARISONBETWEENTHEANALOGDEEPSCWITH channels, we can see in Fig. 4 that our HDA-DeepSC out- +DIFFERENTDIFFUSION-BASEDDENOISERSATSNR=0DB performs all the benchmarks. This indicates that the discrete +signalsof the digital component canaccurately delivercrucial +semantic information for details recovery and the continuous +signals of the analog component can prevent the leveling-off +and cliff-edge effects for lower quantization errors. Besides, +the HDA-DeepSC achieves the best performance in terms of +MS-SSIM,whichmeansthattheimagestransmittedbyHDA- +TableIshowsthecomparisonbetweenanalogDeepSCwith DeepSC have better global quality. This is likely because we +DDPM and DiffSDNet. The proposed DiffSDNet can achieve introducetheFourier-basedlossfunctionthatmakesthemodel +higher PSNR with fewer sampling steps than the DDPM, learn the long-distance dependencies. For the Rician channel +confirmingtheeffectivenessofthedesignedvarianceschedule case shown in Fig. 5, we observe that the DL-based analog +and sampling algorithm. Especially, the PSNR of analog systems are more robust to channel changes due to the high +DeepSCwithDDPMwilldecreaseasthenumberofsampling degree of freedom in continuous signals, in which the HDA- +steps increases. This is due to the high degree of randomness DeepSC is beneficial from the analog component. Moreover, +introduced in the reverse process. thelowbandwidthconsumptionofthedigitalpartallowsusto +uselow-ratechannelcodingtoachieveaccuratedeliverywhile +transmitting a small number of symbols, such as ensuring +C. Communication System Comparisons robustness in the low SNR regimes. This is the reason why +Figs. 4 and 5 report the PSNR and MS-SSIM comparison we assume error-free transmission while training the digital +betweenthevariousmethodsoverAWGNchannelsandRician part. Besides, if the communication environment is terrible, + +---PAGE BREAK--- + +2488 IEEEJOURNALONSELECTEDAREASINCOMMUNICATIONS,VOL.43,NO.7,JULY2025 +Fig.6. PSNRandMS-SSIMperformancefordifferentbandwidthcompressionratiosontheKodakdatasetoverAWGNchannels. +Fig.7. PSNRandMS-SSIMperformancefordifferentbandwidthcompressionratiosontheKodakdatasetoverRicianchannels. +TABLEII codec and train the semantic codec with MSE loss function. +THEABLATIONSOFFOURIER-BASEDCOMPONENT:MSELOSS, The Fourier-based module or loss can improve the quality +MSELOSSWITHFOURIER-BASEDMODULE,ANDMSE of images with more than 2dB in terms of PSNR and MS- +LOSSWITHFOURIER-BASEDLOSS +SSIM due to the long-distance dependencies learning in the +frequencydomain.Besides,weobservethattheFourier-based +loss can largely increase MS-SSIM than the Fourier-based +module. The reason behind that is the Fourier-based module +introduces the additional Fourier-based parameters making it +challenging to further improve its performance. This suggests +that Fourier-based loss can directly capture the global infor- +mation of images without additional parameters and hence as +in which the digital signals cannot be successfully decoded, an attractive loss to improve the global quality of images. +this system will experience the cliff-edge effect due to the +employed entropy coding. This can be improved in several +D. Bandwidth Compression Ratio Comparisons +ways. One is to replace the entropy coding module with the +learning-based quantization module. Another is to introduce Figs. 6 and 7 demonstrate the comparisons for different +error transmission during training. Both methods can lead the bandwidth compression ratios over AWGN and Rician chan- +model to learn to correct the errors in digital transmission. nels at SNR=10 dB. The HDA-DeepSC outperforms all the +Visual examples are presented in Appendix B. benchmarks in terms of PSNR and MS-SSIM. For example, +In Table II, we study the ablations of Fourier-based com- the HDA-DeepSC achieves the same PSNR as separate cod- +ponents by only considering the semantic codec, in which the ings (the BPG with 1/2 LDPC and 16QAM) with a 33% +MSE loss with Fourier-based module means that we insert improvement on bandwidth compression ratio. This suggests +the pluggable Fourier-based modules [39] into the semantic that the HDA-DeepSC can provide a higher data transmission + +---PAGE BREAK--- + +XIEetal.:HYBRIDDIGITAL-ANALOGSEMANTICCOMMUNICATIONS 2489 +Fig.8. PSNRandMS-SSIMperformancefordifferentdigital-analogratiosontheKodakdatasetoverAWGNchannels. +Fig. 9. Visualized examples for different methods transmitted over AWGN channels at SNR=10dB: (a) original image; (b) image recovered by BPG with +1/2LDPCand16QAM;(c)imagerecoveredbyHDA-DeepSCwith0.2DAratiousingunencryptedbits;(d)-(f)imagerecoveredbyHDA-DeepSCwith0.2, +0.87,and3DAratiousingencryptedbits,respectively. +rate than the benchmarks for a given PSNR or MS-SSIM. TABLEIII +Besides, we find that the learning-based methods outperform THEPSNRPERFORMANCEFORTHEENCRYPTEDANDUNENCRYPTED +theBPGintermsofMS-SSIM,indicatingtheneuralnetworks BITSOVERAWGNCHANNELSATSNR=10DB +operate as the better content generator, thereby generating the +image with global consistency. +E. Digital-Analog Ratio Comparisons +Fig.8showsthecomparisonsacrossdifferentdigital-analog +(DA) ratios by changing the ratio between the number of information. This suggests that the analog transmitter oper- +transmitted symbols of digital and analog components, where ates as a continuous signal-based system, thereby effectively +the total number of transmitted symbols is fixed. The larger reducing the quantization errors by decreasing the DA ratio. +DA ratio means more semantic information is transmitted +with the digital transmitter and vice versa. We can observe +F. Data Security +that the PSNR and MS-SSIM decrease as the DA ratio +increases, which is caused by the unavoidable quantization Table III reports the PSNR performance for the encrypted +errorsintroducedbythedigitaltransmitter.Themoresemantic and unencrypted bits, where these terms refer to whether the +information transmitted through the digital transmitter, the encryption algorithm encrypts the bit streams transmitted by +larger the quantization errors introduced to the transmitted the digital transmitter. We assume that the eavesdropper is + +---PAGE BREAK--- + +2490 IEEEJOURNALONSELECTEDAREASINCOMMUNICATIONS,VOL.43,NO.7,JULY2025 +TABLEIV methodtoreducethebitbudgetoflearnedconstellations,such +THERUNNINGTIMEPERIMAGECOMPARISONBETWEEN as achieving low-precision pseudo-analog transmission. The +THEHDA-DEEPSCANDBPG cost is slight performance degradation. +VI. CONCLUSION +Inthispaper,wehaveintroducedaninnovativeHDAseman- +tic communication framework that combines the strengths +of analog and digital semantic communications. Our frame- +incapable of decoding the encrypted bits and only decodes +work aims to overcome the inherent limitations associated +thesemanticinformationtransmittedbytheanalogtransmitter, +with each approach. Building upon the framework, we intro- +wheretheHDA-DeepSCmodelisknowntotheeavesdropper. +duced a robust HDA semantic communication system called +From Table III, the PSNR of encrypted bits is 20dB lower +HDA-DeepSC, specifically designed for multimedia transmis- +compared to that of unencrypted bits, indicating the images +sion. HDA-DeepSC leverages digital communication methods +recovered by encrypted bits are little like the original ones. In +to transmit crucial semantic information, ensuring accurate +other words, the eavesdropper obtains less information from +delivery and data security. Additionally, it utilizes analog +thesemanticinformationtransmittedbytheanalogtransmitter. +communication methods to transmit auxiliary semantic infor- +Besides, the PSNR of encrypted bits slightly decreases as +mation, effectively mitigating the leveling-off and cliff-edge +the DA ratio increases. This suggests that the HDA-DeepSC +effects associated with traditional approaches. We also intro- +effectively safeguards data with few bits while achieving the +ducedanalog-digitalallocationandfusionmodulestoseparate +high PSNR. Visual examples are presented in Figs. 9(c)-(f), +and fuse the digital and analog components, respectively. +where Figs. 9(d)-(f) are the images recovered by encrypted +Besides, we have designed the Fourier-based loss function to +bits.Interestingly,theessentialinformationisprotectedbythe +guide the model in learning the long-distance dependencies +HDA-DeepSC,e.g.thecolor,thebackground,andthetextures, +and combined the rate constraint with the non-parametric, +which proves the effectiveness of the HDA-DeepSC in data +fully factorized density model. Moreover, we have proposed +security. +the diffusion framework enhanced signal detection, named +DiffSDNet, by multiple denoising steps to reduce the noise +G. Computational Complexity level at the low SNR regimes, in which we customized the +The proposed HDA-DeepSC adopts the Swin-Transformer variance schedule and sampling algorithm for wireless com- +as the semantic codec, in which the window multi-head munication environments. The numerical results have proved +self-attention (W-MSA) module has high computational com- the effectiveness of DiffSDNet in denoising and demonstrated +plexity. The computational complexity of W-MSA is O(N the superiority of HDA-DeepSC in terms of robustness, trans- +h w (4C2+2M2C)),inwhichN,C,andM arethenumb × er missionrate,anddatasecurity,especiallyinlowSNRregimes. +of × lay × ers, the width of the layer, and the number of patches, Therefore,theproposedHDAsemanticcommunicationframe- +respectively. The channel codec consists of several dense workshowsgreatpromiseasacandidateforthenewsemantic +layers,thecomputationalcomplexityofwhichisalsolinearin communication paradigm, offering significant potential for +thenumberofpixels.Therefore,thecomputationalcomplexity real-world implementations. +of the proposed HDA-DeepSC is linear encoding/decoding +time in the number of pixels. To complete our discussion APPENDIXA +of computational complexity, we have measured the average DERIVATIONOF(15) +running time per image which is shown in Table IV. We can +Assume the x , i = 1,2, ,N follows the N i.i.d. Gaus- +i +observe that the running time of HDA-DeepSC on the CPU ··· +siansources(variables)withzeromeanandvarianceσ normal +i +is slightly slower than that of BPG on the CPU. However, +distribution,thenthediscreteentropyofx=[x ,x , ,x ] +1 2 N +the GPU can significantly accelerate the running time of ··· +can be written as +HDA-DeepSC, which means it can effectively support some +delay-sensitive applications. H(x)= − E x ∼ p(x) [log 2 p(x)]= − E x ∼ p(x) log 2 i Π = N 1 p(x i ) +(cid:20) (cid:21) +H. Discussion of Hardware Implementation N x 2 += E log 2πσ2 i +It is possible nowadays to implement analog systems with − +i=1 +xi∼ p(xi) +(cid:20) +2 i − 2σ +i +2 +(cid:21) +high-precision digital circuits, called pseudo-analog transmis- (cid:88) (cid:0) (cid:1) +N x 2 N +sion. For example, the pseudo-analog system SoftCast [40] = E i log 2πσ2 . (35) +does not adopt the conventional constellations but modulates +i=1 +xi∼ p(xi) +(cid:20) +2σ +i +2 +(cid:21) +− +i=1 +2 i +thenormalized2DdiscreteFouriercoefficientstothetransmit- (cid:88) (cid:88) (cid:0) (cid:1) +With the (35), we can derive the following relationship, +ted symbols directly. There are a lot of follow-up efforts, and +someofthem[40],[41]havebeenvalidatedonsoftwareradio z 2 ˜z2 +p +(O +la +F +tf +D +o +M +rm +) +s +. T +w +h +i +e +th +ref +o +o +r +r +t +e +h +, +og +it +on +is +al +fe +f +a +r +s +e +i +q +b +u +le +en +t +c +o +y +a +d +c +i +h +v +i +i +e +s +v +io +e +n +hy +m +b +u +ri +l +d +tip +a +l +n +ex +a +i +lo +n +g +g H(z) − H(˜z)= +i=1(cid:18) +E zi∼ p(zi) +(cid:20) +2σ i +i +2 +(cid:21) +− E ˜zi∼ p(˜zi) +(cid:20) +2σ˜ i +i +2 +(cid:21)(cid:19) +(cid:88) +and digital transmission on one hardware platform. For low- log σ i 2 +precision digital circuits, we can employ the quantization − 2 σ˜2 +i=1 (cid:18) i (cid:19) +(cid:88) + +---PAGE BREAK--- + +XIEetal.:HYBRIDDIGITAL-ANALOGSEMANTICCOMMUNICATIONS 2491 +1 +> E z 2 E ˜z2 [14] Y. Bo, Y. Duan, S. Shao, and M. Tao, “Joint coding-modulation for +2σ zi∼ p(zi) i − ˜zi∼ p(˜zi) i digital semantic communications via variational autoencoder,” IEEE +i=1 +(cid:88)(cid:0) (cid:2) (cid:3) (cid:2) (cid:3)(cid:1) Trans.Commun.,vol.72,no.9,pp.5626–5640,Sep.2024. +σ2 +log i , (36) [15] Y.He,G.Yu,andY.Cai,“Rate-adaptivecodingmechanismforsemantic +− 2 σ˜2 communicationswithmulti-modaldata,”IEEETrans.Commun.,vol.72, +(cid:88) i=1 (cid:18) i (cid:19) no.3,pp.1385–1400,Mar.2024. +where σ and σ˜ are the variance of z and˜z , respectively. σ [16] L. Guo, W. Chen, Y. Sun, and B. Ai, “Device-edge digital semantic +i i i i +communication with trained non-linear quantization,” in Proc. IEEE +is the maximum value between σ and σ˜ . +i i 97thVeh.Technol.Conf.(VTC-Spring),Jun.2023,pp.1–5. +σ2 +We can observe the second term of (36), i.e., i, is the [17] C. Liu, C. Guo, Y. Yang, W. Ni, and T. Q. S. Quek, “OFDM-based +σ˜2 +constant. Especially, when z˜ is close to z, the seco i nd term digital semantic communication with importance awareness,” 2024, +arXiv:2401.02178. +will be zero. Therefore, we can drop the second term during +[18] Q.Fuetal.,“Vectorquantizedsemanticcommunicationsystem,”IEEE +training and only consider the first term of (36). With the WirelessCommun.Lett.,vol.12,no.6,pp.982–986,Jun.2023. +Monte Carlo method, the entropy can be written as [19] Q.Hu,G.Zhang,Z.Qin,Y.Cai,G.Yu,andG.Y.Li,“Robustsemantic +communicationswithmaskedVQ-VAEenabledcodebook,”IEEETrans. +H(z) H(˜z) z2 ˜z2. (37) WirelessCommun.,vol.22,no.12,pp.8707–8722,Dec.2023. +− ≈ − [20] H. Gao, G. Yu, and Y. Cai, “Adaptive modulation and retransmission +Consideringthecomputationandtrainingcomplexity,werelax scheme for semantic communication systems,” IEEE Trans. Cognit. +Commun.Netw.,vol.10,no.1,pp.150–163,Feb.2024. +the (37) to the subtraction between z and z˜, which is the (15) +[21] J. Huang, K. Yuan, C. Huang, and K. 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NeuralInf.Process.Syst.,Dec.2020,pp.4479–4488. + +---PAGE BREAK--- + +2492 IEEEJOURNALONSELECTEDAREASINCOMMUNICATIONS,VOL.43,NO.7,JULY2025 +[40] S.JakubczakandD.Katabi,“SoftCast:One-size-fits-allwirelessvideo,” Rebecca Moores Professor with the Electrical and Computer Engineering +in Proc. ACM SIGCOMM Conf., New York, NY, USA, Aug. 2010, Department and the Computer Science Department, University of Houston, +pp.449–450. Houston, TX, USA. His main research targets on the novel game-theory- +[41] X. L. Liu, W. Hu, Q. Pu, F. Wu, and Y. Zhang, “ParCast: Soft video relatedconceptscriticaltoenablingefficientanddistributiveuseofwireless +delivery in MIMO-OFDM WLANs,” in Proc. 18th Annu. Int. Conf. networks with limited resources, wireless resource allocation and manage- +MobileComput.Netw.,Istanbul,Turkey,Aug.2012,pp.233–244. ment, wireless communications and networking, quantum computing, data +science, smart grids, carbon neutralization, and security and privacy. He +received a NSF Career Award in 2010, the Fred W. Ellersick Prize of the +IEEECommunicationSocietyin2011,theBestPaperAwardoftheEURASIP +Journal on Advances in Signal Processing in 2015, the IEEE Leonard G. +Abraham Prize in the field of Communications Systems (Best Paper Award +in IEEEJOURNALONSELECTEDAREASINCOMMUNICATIONS) in 2016, +theIEEEVehicularTechnologySociety2022BestLandTransportationPaper +Award,andseveralbestpaperawardsinIEEEconferences.HewasanIEEE +Huiqiang Xie (Member, IEEE) received the B.S. Communications Society Distinguished Lecturer from 2015 to 2018 and an +degreefromNorthwesternPolytechnicalUniversity, ACM Distinguished Speaker from 2022 to 2025. He has been an AAAS +theM.S.degreefromChongqingUniversity,andthe Fellow since 2019 and an ACM Fellow since 2024. He has been a 1% +Ph.D. degree from the Queen Mary University of Highly Cited Researcher since 2017 according to Web of Science. He is +Londonin2023.From2023to2024,hewasaPost- also the Winner of the 2021 IEEE Kiyo Tomiyasu Award (an IEEE Field +Doctoral Research Associate with The Hong Kong Award), for outstanding early to mid-career contributions to technologies +University of Science and Technology, Guangzhou holding the promise of innovative applications, with the following citation: +Campus.HeiscurrentlyanAssociateProfessorwith Forcontributionstogametheoryanddistributedmanagementofautonomous +Jinan University. He received the 2023 IEEE ICC communicationnetworks. +StudentTravelGrant,the2023IEEEICCBestPaper +Award,andthe2023IEEESignalProcessingSociety +Best Paper Award. He was also the Organizing Committee Co-Chair of +2024 EIECT. He is an Associate Editor of Journal of Communications and +Networks. +Zhijin Qin (Senior Member, IEEE) is currently +an Associate Professor with Tsinghua University, +Beijing, China. She was with the Imperial College +London, London, U.K.; Lancaster University, Lan- +KhaledB.Letaief(Fellow,IEEE)receivedtheB.S. +caster,U.K.;andQueenMaryUniversityofLondon, +degree(Hons.)inelectricalengineeringfromPurdue +London, from 2016 to 2022. Her research interests +University at West Lafayette, IN, USA, in Decem- +includesemanticcommunicationsandsparsesignal +ber 1984, the M.S. and Ph.D. degrees in electrical +processing. She was a recipient of the 2017 IEEE +engineering from Purdue University, in 1986, and +GLOBECOM Best Paper Award, 2018 IEEE Sig- +1990, respectively, and the Ph.D. Honoris Causa +nal Processing Society Young Author Best Paper +degree from the University of Johannesburg, South +Award,2021IEEECommunicationsSocietySignal +Africa,in2022.Heisaninternationallyrecognized +ProcessingforCommunicationsCommitteeEarlyAchievementAward,2022 +leaderinwirelesscommunicationsandnetworks.He +IEEE Communications Society Fred W. Ellersick Prize, and 2023 IEEE +isamemberofUnitedStatesNationalAcademyof +ICC Best Paper Award. She was a Guest Editor of IEEE JOURNAL ON +Engineering, a fellow of Hong Kong Institution of +SELECTEDAREASINCOMMUNICATIONS(JSAC)SpecialIssueonSemantic +Engineers,amemberofIndiaNationalAcademyofSciences,andamember +Communications and an Area Editor of IEEE JOURNAL ON SELECTED +ofHongKongAcademyofEngineeringSciences.Heisalsorecognizedby +AREAS IN COMMUNICATIONS Series. She was also the Symposium Co- +ThomsonReutersasanISIHighlyCitedResearcherandwaslistedamongthe +Chair of IEEE GLOBECOM 2020 and 2021. She is an Associate Editor of +2020top30ofAI2000InternetofThingsMostInfluentialScholars.Hewasa +IEEE TRANSACTIONS ON COMMUNICATIONS, IEEE TRANSACTIONS ON +recipientofmanydistinguishedawardsandhonors,includingthe2022IEEE +COGNITIVENETWORKING,andIEEECOMMUNICATIONSLETTERS. +Communications Society Edwin Howard Armstrong Achievement Award, +2021 IEEE Communications Society Best Survey Paper Award, 2019 IEEE +CommunicationsSocietyandInformationTheorySocietyJointPaperAward, +and 2016 IEEE Marconi Prize Paper Award in Wireless Communications. +He has also been a dedicated teacher committed to excellence in teaching +and scholarship. He received the Michael G. Gale Medal for Distinguished +Teaching(highestuniversity-wideteachingawardandonlyonerecipient/year +is honored for his/her contributions). Since 1993, he has been with The +Hong Kong University of Science and Technology (HKUST), where he +Zhu Han (Fellow, IEEE) received the B.S. degree has held many administrative positions, including the Acting Provost, the +inelectronicengineeringfromTsinghuaUniversity, Head of the Electronic and Computer Engineering Department, and the +Beijing, China, in 1997, and the M.S. and Ph.D. DirectorofHongKongTelecomInstituteofInformationTechnology.While +degreesinelectricalandcomputerengineeringfrom at HKUST, he was the Chair Professor and the Dean of Engineering. He +theUniversityofMarylandatCollegePark,College is well recognized for his dedicated service to professional societies and +Park, MD, USA, in 1999 and 2003, respectively. IEEE, where he has served in many leadership positions. These include +From2000to2002,hewasaResearchandDevelop- the Founding Editor-in-Chief of the prestigious IEEE TRANSACTIONS ON +mentEngineerwithJDSU,Germantown,MD,USA. WIRELESS COMMUNICATIONS. He also served as the President of the +From 2003 to 2006, he was a Research Associate IEEECommunicationsSociety(2018–2019),theworld’sleadingorganization +with the University of Maryland at College Park. for communications professionals with headquarters in New York City and +From 2006 to 2008, he was an Assistant Professor membersin162countries.HealsoservedasamemberfortheIEEEBoard +with Boise State University, Boise, ID, USA. He is currently a John and ofDirectors. \ No newline at end of file diff --git a/paper3.txt b/paper3.txt new file mode 100644 index 0000000..27e096c --- /dev/null +++ b/paper3.txt @@ -0,0 +1,2873 @@ +IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 2965 +Resource Allocation in Wireless Semantic +Communications: A Comprehensive Survey +Chujun Zhang , Linyu Huang , Member, IEEE, and Qian Ning +Abstract—Withtheadventofsixth-generationmobilecommu- Metaverse require wireless communication networks to trans- +nicationtechnology(6G)andtheemergenceoffutureapplication mit huge amounts of data. Wireless communication networks +scenarios such as Metaverse and digital twin (DT), the exist- +mustachieveanextremelylowtransmissiondelayinscenarios +ing traditional wireless communication technology based on +such as autonomous driving and telemedicine. The emergence +Shannon’s information theory has not been able to meet the +increasing demand for data transmission. Semantic commu- of these applications presents new challenges to traditional +nications (SemCom), which greatly reduces the amount of communication systems. +information transmitted and alleviates the burden of communi- In the face of such a large communication load, how +cation by transmitting the meaning behind the information, has +can one go beyond Shannon’s limit to the future? Inspired +been considered a promising 6G enabler. SemCom’s resource +by the three levels of the previous communication problem, +allocation is critical to the system’s reliability and effectiveness. +Compared to traditional wireless communication systems, the a new communication paradigm, semantic communication +system architecture and performance metrics of SemCom have (SemCom) [2], [3], [4], has been proposed to shift the com- +undergone significant changes, making it difficult for traditional munication paradigm to the semantic and effectiveness levels. +resource allocation strategies to adapt well to this new architec- +In traditional communication systems, data is compressed +ture. However, the issue remains unresolved and inadequately +bythesourceencoder,andredundancyisaddedtothechannel +researched.Inordertoprovideresearcherswithvaluableinsight +to promote follow-up research, this paper reviews the latest encoder to improve its robustness to interference/noise in the +research results in recent years and presents an overview of channel. At the destination, a reverse process is performed to +research progress in the field of resource allocation in wireless recover the original sent data. The transmission and reception +SemCom. +of signals do not involve any intelligence and the semantic +Index Terms—Performance metrics, resource allocation, information is omitted [5]. +semantic communications, semantic similarity. However, in a SemCom system, the semantic source and +destination are intelligent agents that can perform various +highly intelligent algorithms. Semantic coding replaces tradi- +I. INTRODUCTION tional source coding through deep learning (DL) and other +A. Context technologies to extract semantic information. Unlike tra- +ditional communication systems, which are easily affected +IN1949,WeaverexpandedShannon’stheorytothreelevels: +by channel conditions, SemCom performs well, especially +technical level, semantic level, and effectiveness level [1]. +at low signal-to-noise ratios (SNR), because only semantic +The lowest level is the technical level, which is mainly +information is transmitted. Goal-oriented SemCom or task- +responsible for the accurate and effective transmission of +oriented SemCom is a subset of SemCom that pays more +information symbols; the middle level is the semantic level, +attention to the effectiveness level. Specifically, it focuses on +which points to the transmission of information symbols to +the efficient use of semantic information for the successful +convey the desired meaning; the upper level is the effective- +execution of tasks at a suitable time [6]. The receiver in a +ness level, which aims at effectively performing intelligent +goal-oriented SemCom is interested in the significance and +tasks and providing the needed communication efficiency on +effectiveness (semantics) of the transported source message +the lower two levels. Traditional communications operate at +to achieve a certain task or goal [7]. In summary, SemCom +the technical level, focusing on accurate bit transmission. +is becoming an excellent solution to the above questions. +However, they transmit all information, including useless and +SemCom is also regarded as a key enabling technology for +irrelevant data, to the receiver, leading to channel resource +6G, and it is an important step towards the future of wireless +waste. As sixth-generation mobile communication technology +communication. +(6G) emerges, scenarios such as Digital Twin (DT) and +Received 25 February 2025; revised 23 April 2025 and 17 June 2025; +accepted 30 July 2025. Date of publication 4 August 2025; date of current B. Resource Allocation +version 2 January 2026. This work was supported by the National Natural +In general, resource allocation refers to a set of method- +ScienceFoundationofChinaunderGrant61801318.(Correspondingauthor: +LinyuHuang.) ologies to achieve goals by efficiently allocating resources +The authors are with the College of Electronics and Information and using resource allocation methods based on resource +Engineering,SichuanUniversity,Chengdu610065,China(e-mail:zhangchu- +availability. The resource allocation problem in wireless com- +jun@stu.scu.edu.cn;lyhuang@scu.edu.cn;ningq@scu.edu.cn). +DigitalObjectIdentifier10.1109/COMST.2025.3595168 munications and SemCom is mapped into a mathematical +1553-877X(cid:2)c 2025IEEE.Allrightsreserved,includingrightsfortextanddatamining,andtrainingofartificialintelligence +andsimilartechnologies. Personaluseispermitted,butrepublication/redistributionrequiresIEEEpermission. +Seehttps://www.ieee.org/publications/rights/index.htmlformoreinformation. +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2966 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +clarifying the unique contribution of this work. Table I pro- +vides a comparison between our survey and representative +prior works. Although there are some recent surveys on +resource allocation in other communication scenarios that +provided us with great insights, such as edge comput- +ing [16], fifth-generation mobile communication technology +(5G)-and-beyondmobileedgecomputing(MEC)[17],Internet +of Things (IoT) enabled vehicular edge computing [18], +energy-efficient Orthogonal Frequency Division Multiplexing +(OFDM) enabled networks [19], and ultra-dense networks +(UDNs) [20]. Resource allocation is a critical and under- +explored aspect of SemCom, it significantly differs from +traditional communication systems, as it involves unique +allocatable resources like semantic fidelity and computation +overheadforsemanticprocessing,alongsidetraditionalfactors +such as bandwidth and power. Moreover, SemCom introduces +novel performance metrics that will make the object function +Fig.1. TheintegratedframeworkofresourceallocationinSemCom. more complicated, which we will provide a more explicit +descriptioninSectionIII.Thesedifferenceshighlighttheneed +to focus specifically on resource allocation in SemCom, as +optimization problem by modeling the network structure and +existing surveys tend to overlook the unique challenges and +designing the objective function. In resource allocation, the +optimization strategies required in this domain. By dedicating +available resources for allocation are optimization variables; +our review entirely to this topic, our aim is to fill this +the availability of resources and other inherent conditions are +gap and provide a comprehensive and systematic overview +constraints; the objective function is the function to evaluate +of how resource allocation can be effectively addressed +the system performance of achieving a specific goal; the +within the context of SemCom. Therefore, we review from +resourceallocationalgorithmisacombinationofoptimization +multiple perspectives, including SemCom network models, +techniques that are used to solve this optimization problem. +performance metrics, resource allocation optimization algo- +TheresourceallocationalgorithmsinSemComcanbedivided +rithms, as well as challenges and future research directions, +into centralized and distributed ways. The centralized algo- +providing researchers with a new, comprehensive, and rich +rithm includes techniques based on convex optimization and +perspective. +other mathematical methods, and based on deep reinforce- +ment learning (DRL), etc. The distributed algorithms include +techniques based on multi-agent deep reinforcement learning D. Research Methodology +(MADRL) and matching theory, etc. The integrative frame- +In this subsection, the process followed to collect the +workofresourceallocationinSemComisillustratedinFig. 1. +references used in this study is described. The methodology +However, compared to the traditional wireless communica- +includestheselection,inclusion,andexclusioncriteriaapplied +tionsystem,theSemComsystemarchitectureandperformance +toensurethequalityandrelevanceofthereferences.Thesteps +metrics have undergone tremendous changes, making it diffi- +followed in the research process are as follows: +cult for traditional resource allocation strategies to adapt well +• Literature Search: The search was performed using +to this new architecture. In the next section, we willprovide a +databases such as Google Scholar, IEEE Xplore, and +moredetaileddescriptionofthedifferencebetweentraditional +ScienceDirect. The primary focus was on peer-reviewed +communication and SemCom in terms of resource allocation +journal articles, conference papers, books, and other +and why it is important. +reputablesourcesrelatedtoSemCom,resourceallocation, +optimization, and wireless communication networks. +C. Related Surveys and Motivation • InclusionCriteria:Tobeincludedinthestudy,references +The development of SemCom has led to the publication must meet the following criteria: 1) Published in a peer- +of numerous surveys in recent years. Existing surveys on reviewed journal or conference proceedings. Directly +SemCom may address resource allocation to some extent, related to resource allocation in SemCom networks +they mostly provide a global perspective of SemCom and or relevant areas such as optimization, deep learning +often focus on broader aspects such as system architectures, techniques, and wireless communications. 2) Except +semantic information theory, enable techniques or general for some classic and fundamental literature, references +applications of SemCom [3], [4], [5], [6], [8], [9], [10], should be published within the last 10 years to ensure +[11], [12], [13], [14], [15]. However, our work is the first to that the research is up-to-date and relevant. 3) For +present a dedicated and in-depth review of resource allocation researchpapers,theoreticalandempiricalstudiesmustbe +in SemCom systems. To highlight this distinction, we have included. +added a comparative table that outlines the resource allo- • Exclusion Criteria: References that met any of the +cation aspects covered (or not) by existing surveys, thereby following conditions were excluded from the review: +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2967 +TABLEI +COMPARISONOFEXISTINGSEMCOMSURVEYSANDTHISWORK +1) Studies that were completely not related to SemCom +or resource allocation; 2) Non-peer-reviewed sources or +articleswithoutsufficientmethodologicalrigor.3)Studies +published more than 10 years ago unless they introduced +foundational theories or seminal works that remain rele- +vant to current research. +• Information Extraction and Analysis: After finalizing the +selected references, the key information was extracted +andanalyzedinresourceallocation.Thisincludedunder- +standing the research objectives, methodologies used, +findings, and how each study contributed to advance the +understanding of SemCom resource allocation. +Fig.2. Thedistributionofpaperssurveyedbyyearandsource. +E. Contributions and Organization +This paper reviews the current state of research in the +period2021-2025(February)onresourceallocationinwireless divided into end-to-end (E2E) and multi-user situations. +SemCom.Fig. 2showsthedistributionofthearticlessurveyed We also investigated the use of the next generation of +by year and source. The report encompasses arXiv articles multipleaccess(NGMA)technologiesandhybridseman- +and website articles, while the conference category includes tic/bit communications in SemCom resource allocation. +conference and symposium papers, and the journal category WegivetheoverviewofresourceallocationinSemCom, +includes journal and magazine articles. The contributions of thereby explaining the reason why resource allocation +this paper can be summarized as follows: in SemCom is important for the theoretical perspective +• We first explain the basics of resource allocation in and reality perspective, clarifying the unique specific +SemCom and introduce the network model in the cur- challenges that inherently exist in the resource allocation +rent literature on SemCom resource allocation, which is of SemCom. +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2968 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +Fig.3. Roadmapofthesurvey. +• The construction of the objective function is the core of summarize the different centralized and distributed resource +the optimization problem modeling, so we introduce the allocation optimization algorithms in detail. Section VI points +performance metrics in the SemCom resource allocation out the challenges and possible future research directions. +in detail. We mainly summarized the construction meth- Finally, Section VII summarizes this survey. Fig. 3 shows the +ods into two types. One is utilizing traditional resource organization and structure of this survey paper. +allocationperformancemetrics,suchasdelayandenergy +consumption. The other type is based on the semantic +II. BASICSOFRESOURCEALLOCATIONINSEMCOM +similarity, establishing new performance metrics. +ThissectionwillexplainthebasicsoftheSemComresource +• We discuss in detail different optimization algorithms +allocation problem. We provide an overview of SemCom, +in the allocation of SemCom resources, which are +followed by a review of the fundamental network mod- +divided into centralized and distributed algorithms. +els found in various SemCom resource allocation studies. +Centralized algorithms include algorithms based on +Furthermore, we give an explicit contrast between bit-level +convex optimization and other mathematical methods, +and semantic-level modeling in Table III, which provides a +algorithms based on DRL, and heuristic algorithms. +side-by-side comparison between the two paradigms, high- +Distributed algorithms include methods based on +lightingtheirrespectivetargets,metrics,modelingapproaches, +MADRL, matching theory, and auction. These meth- +and optimization goals. Next, we provide an overview of +ods are summarized in three comprehensive tables for +resource allocation in SemCom. Besides, we give the taxon- +comparison. +omy of system framework establishment in Fig. 5. Lastly, we +• Through the analysis presented above, we propose future +summarize the literature in Table IV. +researchdirectionsandseveralchallengestobesolvedin +the field of SemCom resource allocation. +The remainder of this paper is organized as follows. A. Overview of SemCom +Section II introduces the basic architecture of the SemCom Traditional communications aim to reach the technical +resource allocation problem. Section III presents traditional level, which means achieving a high data transmission rate +performancemetrics,thedefinitionofsemanticsimilarity,and and a low symbol error rate. However, the basic idea of +new semantic-based performance metrics. Sections IV and V SemCom is to extract the “meanings” or “features” of the +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2969 +Fig.4. Acomparisonbetweenthebasicend-to-endnetworkarchitectureoftraditionalcommunicationandSemCom. +source and “interpret the semantic information” at a des- infer the received information to complete the recovery +tination. Therefore, SemCom surpasses traditional bit-level of the received semantic information. +transmissiontoachievesemantic-leveltransmission,leadingto Currently, most of the research literature is focused on three +significant changes in the design of the network architecture. types of sources: text signal, image signal, and speech signal. +Moreover, thereis very littleliteraturethat points the research +1) Basic End-to-End SemCom: A comparison between the direction to multi-modal tasks [22]. +basic end-to-end (E2E) network architecture of traditional Text: Text SemCom systems have been widely stud- +communication and SemCom is shown in Fig. 4. Fig. 4a ied. Various DL techniques are used to represent the +illustrates the typical traditional E2E communication archi- underlying meaning of texts. DL-enabled semantic codecs +tecture, where the source encoder receives the transmitted have been through the early Long Short Term Memory +dataandcompressesitinitially,partiallyeliminatingredundant (LSTM)-based models [23], [24], to today’s Transformer- +information through source encoding. The channel encoder basedmodels[25], [26].In2018,Farsadetal.[23]proposeda +adds redundancy in various coding ways to combat noise jointsource-channelcoding(JSCC)schemefortextSemCom, +and attenuation in the channel, thereby enhancing its anti- in which the encoder and decoder are implemented by two +interference capability and error correction ability. At the LSTM networks. Compared to the single source channel +destination, a reverse process is conducted to recover the coding (SSCC) scheme, the DL-based JSCC scheme per- +original sent data. We can see in Fig. 4b that SemCom forms better [23]. In 2021, Xie et al. [25] proposed the +primarily differs from traditional end-to-end architecture in DeepSC framework by fine-tuning the basic structure of +three key ways. Transformer[27].DeepSCcanadapttodifferentchannelenvi- +• SemanticCoding:ASemComsystemextractstheseman- ronments, perform well under low SNR, and have excellent +tic information (features) from the original data through robustness. The author of [28] proposed a semantic extraction +semantic coding enabled by technologies such as DL scheme based on the entity recognition model (NER) and +and then encodes these features for channel coding. Due LSTM that transforms the transmitted sentence into multiple +to the implicit meaning inherent in the message under triplets of semantic importance, and important triplets will be +consideration, the amount of redundant data removed is allocated more transmission resources to improve reliability. +significantly greater than that achieved by source coding. The authors of [29] introduce a life-long model updating +Not like semantic segmentation in computer vision, in approach in which the receiver can learn from previously +SemCom, all communication parties must maintain a received messages and automatically update the rules to +high degree of consistency in semantic expression and reasoningforhiddeninformationwhennewunknownsemantic +understanding, which poses a challenge to semantic entities and relations have been discovered. +compression. Image: The image SemCom system is similar to the text +• Knowledge Base: Another important feature of SemCom SemCom system, and there is much research on it. In con- +is that it is a knowledge-based system [21]. This means trast to text systems, image SemCom systems extract the +that semantic source and semantic purpose can be like original image’s features (which, in this context, represent +the human brain, through self-learning to establish their the image’s “meaning”) and extensively utilize convolutional +own background knowledge bases (KBs) to guide the neural networks (CNNs). In addition, in many task-oriented +transmitter to obtain multi-level semantic knowledge SemCom systems (such as image classification tasks), the +descriptionofsourcedata,semanticinference,estimation image does not need to be reconstructed at the receiver. In +of transmission environment, and semantic requirements 2019, Bourtsoulatze et al. [30] first proposed an end-to-end +of downstream tasks. The system performs semantic imagetransmissionsystemusingCNN’sJSCCscheme,which +coding and directs the receiver to execute the inverse has better performance than traditional image transmission +process, known as semantic decoding. methods. In 2022, Dong et al. [31] proposed a layer-based +• Semantic Decoding: Based on technologies such as semantic communication system for images (LSCI), and +semantic KBs and DL, the receiver can understand and the concept of semantic slice-models (SeSM) is proposed +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2970 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +to enable flexible model resemblance under the different samples using CNN and the gated recurrent unit (GRU)- +requirementsofthemodelperformance,channelsituation,and based bidirectional RNN (BRNN) modules. In the image +transmissiongoals.In2023,Lokumarambageetal.[32]imple- SemCom systems [30], [31], [32], [33], [39], networks +mented a semantic communication-based end-to-end image such as CNN and GAN are often used, and the input +transmission system, where a pre-trained GAN network is is a n-dimensional image, not a sequence like text or +used at the receiver as the transmission task to reconstruct speech. Simulation results show that SemCom performs +the realistic image based on the semantic segmented image well especially under the low SNR. This is because the +at the receiver input. Kadam and Kim [33] proposed a joint extractedsemanticfeaturesreduceredundancywhichwill +CNN-LSTM-based SemCom model in which the semantic use more channel resources. After semantic extraction, +encoder of a camera extracts the relevant semantics from the high-level semantic representations are less sensitive to +raw images, resulting in a novel approach to the problem of noise, which makes the SemCom system more robust. +predicting vehicle counts. • Knowledge Graph-based semantic extraction: It extracts +Speech: Unlike the previous two modes of the SemCom structured information as semantic triples (subject, pred- +system, the speech signal possesses more complex icate, object) to form a knowledge graph, which +performance characteristics, including speech speed, volume, enhances interpretability and enables reasoning but +tone, and dialect, all of which can express the same meaning. requires high construction and maintenance costs. The +The general approach is to convert the speech into text for semantic information of a knowledge graph is typically +processing. However, the same text information expressed expressed as triples in the form of (head, relation, +in different intonations will produce different meanings. tail). From a piece of text data, multiple triples can be +Therefore, the process of voice semantic transmission is more extracted, and these triples can be used to characterize a +complex and challenging to manage [34], [35]. The majority knowledge graph. The knowledge graph extracted from +of the source modes in SemCom’s resource allocation are text each sample data Tn is represented as +(cid:4) (cid:5) +and image modes. Currently, there is no relevant research on +the allocation of resources for the SemCom speech system. Gn = ε1 n ,ε2 n ,...,εm n ,...,εM n , (2) +In the following content, we will introduce and compare +these papers comprehensively and organize them in tables for +whereεm +n isthem-thtripleinknowledgegraphGn,M is +reference. the total number of triples. The triple +εm +n can be written +in the following form: +2) Mathematical System Modeling of SemCom: While the +previous section has highlighted the core components of εm n =(hn m,rn m,tn m ), (3) +semanticcommunication,itisequallyimportanttounderstand +how these elements integrate into a mathematical frame- where hn +m +is the head entity of triple +εm +n , tn +m +is the tail +m +work. We will introduce some essential parts of mathematical entity, and rn is the relation of head and tail entities. +modelinginpapers,mainlyonsemanticextractionandseman- For text, the work in [40] used an information extrac- +tic metrics (it will be discussed thoroughly in Section III). tion system to extract semantic triples from texts and +• NN features-based semantic extraction: It utilizes deep modeled as KGs, and the receiver used a graph-to-text +learning models for end-to-end semantic encoding, generative algorithm to recover the original texts based +offering strong contextual understanding but lacking on the received triples. In [41], a cognitive text semantic +interpretability and explicit semantic relationships. In communication framework is proposed by exploiting +such an approach, the encoded symbol stream can be knowledge graph. For image, the scene graph (SG) is +represented by a visual KG that describes visual relationships between +(cid:2) (cid:3) entities,theauthorsin[42]and[43]usedobjectdetection +x=Cα Sβ(s) , (1) and RE algorithms to extract SG from images. +3) Multi-User SemCom and Multiple Access Techniques: +where, Sβ(·) is the semantic encoder network with The previous section introduces several end-to-end SemCom +parameter set β and Cα(·) is the channel encoder with systems.However,alltheabovesystemsdonotinvolvemulti- +parameter set α, the specific networks are various in user transmission. In general, the connection density of 5G is +different systems. In text SemCom systems [25], [36], 106devicespersquarekilometer,whiletheconnectiondensity +networks such as Transformer, BERT, or LSTM are ofthe6Gnetworkwillincreaseto10timesthatof5G,andthe +utilized for semantic extraction, s = [w1 ,w2 ,...,wL] regional traffic density should be 100 times that of 5G, which +denotes the original sentence, wl represents the l- requires a significant improvement of spectral efficiency [5]. +th word in each sentence. In speech SemCom Moreover, the knowledge base within the SemCom system +systems [34], [37], [38], ResNet, Transformer, CNN and may vary significantly. Therefore, from a more realistic point +RecurrentNeuralNetwork(RNN)areutilizedforseman- ofview,itisnecessarytodesignamulti-userSemComsystem. +ticextractionindifferentstudies,theinputs isthespeech Notably, we only survey the multi-user SemCom system in +sample sequence, s =[s1 ,s2 ,...,sW] with W samples, papers on resource allocation in SemCom, not all SemCom- +where sw is the w-th item in s and it is a scalar value. related papers. +In the DeepSC-ST system [38], text-related semantic In the resource allocation problem of multi-user SemCom, +features are extracted from the input speech spectrum the classical multiple access (MA) techniques such as +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2971 +TABLEII +THECOMPARISONOFDIFFERENTNGMATECHNIQUES +frequency division multiple access (FDMA) [36], [44], [45], via SIC, user-k decodes its desired private stream sk, so the +[46],[47],[48],orthogonalfrequencydivisionmultipleaccess private rate of user k is +(OFDMA) [40], [49], [50], [51], [52], [53], [54], [55], (cid:6) (cid:8) (cid:8)hHp (cid:8) (cid:8)2 (cid:7) +[ +(T +56 +D +] +M +, [ +A +57 +) +] +[ +, +61 +[5 +], +8] +[ +, +62 +[ +] +59 +te +] +c +, +h +[ +n +6 +i +0 +q +] +ue +o +s +r +a +t +r +i +e +m +m +e +o +d +s +i +t +v +ly +is +u +io +s +n +ed +m +.H +u +o +lt +w +ip +e +l +v +e +er +a +, +c +w +ce +i +s +th +s Rk =Blog2 1+ (cid:8) +(cid:8)h k p +k +j +(cid:8) +(cid:8)2 +k ++Nk +, (6) +the continuous development of communication technology, +so that the achievable total rate of user k is +researchers have begun to explore the application of the +combination of next-generation multiple access (NGMA) and Rk,tot =Ck +Rk . (7) +SemCom in resource allocation. Before comparing different +In [64] and [65], the authors used SDMA as the multiple +MA techniques in papers on resource allocation in SemCom, +access method and established an SDMA-based multiuser +wesummarizedthesethreekeyNGMAtechniquesinTable II. +probabilistic SemCom (PSC) framework that considers both +As in Table II, spatial division multiple access (SDMA) +transmission and computational consumption. The authors +treats the interference of other users fully as noise. Non- +of [61] proposed a new semantic-aware resource allocation +orthogonal multiple access (NOMA) will employ successive +scheme in the integration of the radio frequency energy +interference cancellation (SIC) at one user to fully decode the +harvesting (EH), cognitive radio (CR), and NOMA scenario. +interference. Rate splitting multiple access (RSMA), based on +An uplink network consisting of multiple primary users (PU) +theconceptofratesplitting(RS),isconsideredtobeapromis- +using TDMA and a secondary user (SU) using NOMA and +ing physical layer transmission paradigm for non-orthogonal +PUmultiplexingspectrumisconsidered.Inthebackgroundof +transmission, interference management, and multiple access +PSC, the work in [66] studied the joint communication and +strategies in 6G. The main idea of RSMA is to divide +computation design in the reconfigurable intelligent surface +user messages into common and private parts (sc and sk) +(RIS)-assisted industrial Internet of Things (IIoT). +and to be able to partially decode interference and partially +Compared to SDMA and NOMA, the research on resource +treat interference as noise, which is in stark contrast to the +allocation in the combination of RSMA and SemCom is +extreme interference management strategies used in SDMA +obviously more [67], [68], [69], [70], [71], [72]. In [67], +and NOMA. The flexibility of RSMA makes it perform well +the optimization problem of the energy consumption of the +at all levels of interference [63]. In RSMA, pk and pc are +downlink SemCom network with RSMA is studied. The +the power allocated to private messages and the common +authors of [69], [70] focused on the PSC framework based +message. The common stream sc is decoded first by treating +on RSMA; reference [70] expanded the work of [64], and +the interference from private streams s1 and s2 as noise. As +the multiple access mode was changed from uplink SDMA +sc contains part of the intended message as well as part of +to downlink RSMA, while the authors of [69] paid more +the message of the interferer, it enables the ability to partially +attention to the energy-saving design of the PSC system. +decode interference and partially treat interference as noise. +The simulations of the above literature [68], [70] compare +The instantaneous rates for decoding the common streams at +the SDMA and NOMA-based schemes. The results show +user-k are +(cid:6) (cid:7) that the RSMAbasedschemeperformanceisthebestinterms +|h p |2 +Rc,k =Blog2 1+ +|h k p 1 |2 + +k +|h k +c +p 2 |2 +Nk +. (4) of +4 +to +) +ta +H +l +y +s +b +e +r +m +id +anti +S +c +em +tra +a +n +n +s +t +m +ic- +is +B +s +i +i +t +on +C +ra +o +t +m +e. +munication: While most +research on resource allocation focuses solely on SemCom +To guarantee that common message sc is decoded by both +itself, the coexistence of SemCom and bit communication +users, the common rate shall not exceed +(BitCom) modes has also received attention [44], [45], [73], +Rc =min{Rc,1 ,Rc,2 }. (5) [74], [75], [76], [77], [78]. SemCom is more suitable for low +signal-to-interference-plus-noise ratio (SINR) and resource- +Denote Ck as the common rate portion of user-k: C1+C2 = constrained scenarios, while BitCom performs well in high +Rc .Oncesc isdecodedandremovedfromthereceivedsignal SINR regions. Moreover, it is not possible to completely +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2972 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +TABLEIII +COMPARISONOFMODELINGANDFRAMEWORKS:TRADITIONALVS.SEMANTICCOMMUNICATION +replace BitCom’s current huge infrastructure and user bases (suts/s, which will be discussed in the next section), which +at once. In the future, hybrid semantic/bit communication is unified into the semantic correlation measure to measure +networks will become an inevitable and persistent example of the network performance. Compared with the combination +intermediate networks [78]. The authors of [73] proposed a mode of SemCom and BitCom in [75], [78], the works +novel multi-carrier E2E system that combines both semantic in[44], [45], [74]bothstudiedanotherformofcoexistenceof +and Shannon (bit) communications, in which both the BS and SemCom and BitCom separately in the downlink and uplink +theusercancommunicatebychoosingtoutilizeeitherbitCom transmission. A semantic relay (SemRelay)-aided system was +or SemCom on each subcarrier. For resource allocation in proposed. We use the uplink transmission scenario in [74] for +the coexistence of semantic and bit communication networks, explanation: from the users to SemRelay using BitCom, from +the focus is how to combine the measurement of the two. SemRelay to the BS using SemCom. In the User-SemRelay +us +In [78], a bit-to-message (B2M) conversion function is used link, FDMA is adopted, the achievable rate Rn is: +to convert the rate metric into the capacity of the semantic (cid:6) (cid:7) +c +p +h +e +a +r +n +u +n +n +e +i +l +t +( +t +i +i +. +m +e. +e +, +, +th +m +e +s +a +g +c +/ +h +s) +i +, +ev +le +a +t +bl +R +e +i +m +j( +e +· +s +) +sa +d +g +e +e +no +ra +te +te +th +in +e +u +B +n +2 +it +M +so +f +f +u +m +nc +e +t +s +io +sa +n +g +o +es +f +Rn us =Bn us log2 1+ |h +B +n u +n u +s +s +|2 +N +p +0 +n u , (11) +the SemCom link between mobile user (MU) i and BS j, its +instantaneous achievable message rate in time slot t should be where N0 is the power spectral density of the additive white +Mi S j(t)=β ij(t)R ij (cid:2) bij log2(1+γ ij(t)) (cid:3) . (8) G of au u s s s e ia r n n n , o h is n u e s (A d W en G ot N es ), t p h n u e d c e h n a o n t n e e s l th g e ai t n ran f s ro m m iss u io s n er po n we to r +us +Here, β ij(t), bij(t), and γ ij(t) represents the knowledge- SemRelay and Bn denotes the bandwidth allocated to the +matching degree, bandwidth, and SINR between MU i and its link. The transmission delay for each user n is given by +communicationcounterpartatslott.ComparedtotheSemCom tn us = R D u n s . Here, Dn is the volume of text data in bits. The +n +link,theinstantaneous achievable messagerateoftheBitCom computation time cost for semantic compression at SemRelay +c +link in slot t is given by is t , The achievable rate of the SemRelay-BS link is: +(cid:2) (cid:3) (cid:6) (cid:7) +Here, bij(t +M +), +i B j +an +(t +d +) +γ += +ij( +ρ +t +ij +) +R +de +ij +no +b +t +i +e +j +s +lo +th +g +e +2( +s +1 +am ++ +e +γ +t +i +h +j( +in +t) +g +) +a +. +s in Eq. ( +( +8 +9 +) +) +, +R sb =B sb log2 1+ |h +B +s +s +b +b +| +N +2 p +0 +s , (12) +and ρ ij is an average B2M transformation ratio to measure +s sb +network performance with a message-related metric unified where, pn denotes the transmission power of SemRelay, hn +sb +with SemCom. denotes the channel gain from SemRelay to BS, and Bn +IftakingbothSemComandBitComintoaccount,useyij to denotes the bandwidth allocated to the link. The transmission +denote the communication mode selection (yij = 1 represents delay for SemRelay is given by t +sb += +D +R +S +s +e +b +m. +Here, D +Sem +that the SemCom mode is selected for the link between MU is the total number of bits for the compressed semantic +c Sem +i and BS j, and yij = 0 indicates that the BitCom mode is information. The explicit expression of t and D can be +all +selected), the time-averaged message rate of each link is found in [74]. So the overall latency t is +Mij = 1 (cid:9)N (cid:10) yijMi S j(t)+ (cid:2) 1−yij (cid:3) Mi B j (t) (cid:11) . (10) t all =max{tn us,∀n}+t c +t sb. (13) +N +t=1 Another difference from [75], [78] is that [44] and [45] +In [75], the equivalent transformation method in [36] is transformthesemanticrateintothebitratetounifythesetwo +usedtotransformthebitrateintotheequivalentsemanticrate rate metrics into a bit-based metric (bit/s). +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2973 +B. Overview of Resource Allocation in SemCom • Reality perspective: In the context of 6G, the amount of +data generated by terminal devices around the world is +We have given a brief description of resource allocation in +explosively increasing. Coordinating limited resources to +SectionI.Wearenowgivingamoredetaileddescriptionofthe +betterprocessthesedatarequiresanappropriateresource +differencebetweentraditionalcommunicationandSemComin +allocation strategy. Data from different application sce- +terms of resource allocation, as well as why it is important. +narios may have different service requirements. Vehicles +1) The Difference With Traditional Communications: +in autonomous driving scenarios need to process data +• Optimization Problem: Compared with traditional wire- +in milliseconds to ensure traffic safety. Therefore, ultra- +lesscommunication,SemCom’snetworkarchitecturehas +low latency is its main goal. Semantic sensing systems +changed in many aspects, from codec level to multiple +assisted by uncrewed aerial vehicles (UAVs) usually +access modes. Due to the inexplicability of neural +pay more attention to the long battery life and expect +networks, it is difficult to derive closed-form expressions +to achieve low energy consumption. In addition, some +of some objective functions or variables. Therefore, the +mobile devices and IoT devices are designed to achieve +constructed optimization problem, from the objective to +lowdataprocessingcostsorachievethebestusersatisfac- +the constraints and optimization variables, differs signif- +tion. Therefore, appropriate resource allocation strategies +icantly from the traditional architecture. +are needed to meet these diverse needs. +• Optimization Algorithm: As artificial intelligence and +machine learning technology continue to advance, an +3) Specific Challenges of Resource Allocation in SemCom: +increasing number of intelligent methods have emerged +SemCom brings fundamental shifts to the modeling, evalu- +to address resource allocation problems. For exam- +ation, and optimization of wireless communication systems. +ple, neural networks are used to approximate the +These shifts give rise to several unique challenges that are +function in which closed-form expressions cannot be +rare or nonexistent in traditional communications and funda- +obtained,anddeepreinforcementlearning(DRL)hasalso +mentally affect how resource allocation must be performed. +become a powerful tool for solving complex resource +Although Section VI will discuss open research problems +allocation problems in recent years [79], [80], [81]. +and promising future directions for SemCom, this subsec- +Though traditional methods like mathematical and con- +tion focuses on the specific and practical challenges that +vex optimization-based algorithms are still widely used, +currentlyariseinexistingSemComsystemdesignsandimple- +resource allocation in SemCom is more applicable +mentations. These challenges reflect the inherent complexity +to intelligent methods, and many papers tend to use +and unique characteristics of SemCom. By clarifying these +intelligentmethod-basedalgorithms.Wewillgiveacom- +concrete issues, we lay the foundation for understanding +prehensiveintroductiontothesealgorithmsinSectionIV. +why the optimization techniques in SemCom (which will be +2) The Reason Why Resource Allocation in SemCom is introducedinSectionsIVandV)arenecessary.Thesespecific +Important: challenges can be summarized as follows: +• Theoretical perspective: Firstly, from the perspective of • Tradeoff Caused by Semantic Compression Ratio: +thenetworkmodel,SemComhasalotofnewmodulesto There are many tradeoffs, such as the energy-latency +consider,suchasthesemanticencoderandtheknowledge tradeoff and the accuracy-efficiency tradeoff, that +base.MostSemComsystemsuseDLtechniquestoadopt already exist in traditional communications. However, +semantic extraction. Neural networks will bring about a SemCom introduces the new resource type, the seman- +lotofinexplicabilityandcanresultinthelackofaclosed tic compression/extraction ratio, which directly affects +form of part of the objective function. Moreover, as it communication, computation, and semantic fidelity. For +involves unique allocatable resources such as semantic instance, a higher compression ratio reduces the data +fidelity and computation overhead for semantic pro- size for transmission and saves transmission delay and +cessing. Optimization algorithms to optimize these new energyconsumption(communicationloadreduction),but +semantic-related variables directly have a great influence it lowers the semantic fidelity and task accuracy and +on the whole system performance. Besides, traditional needs more computing resources to process the seman- +performance metrics do not consider the meaning of tic extraction and recover, which results in the local +information. Using traditional performance metrics for extraction latency and energy consumption at the trans- +resource allocation may even lead to a decrease in mitter, the recover latency and energy consumption +system performance. Therefore, developing new metrics at the receiver (computation load increase). Moreover, +that match the characteristics of SemCom and designing for intelligent tasks, a higher compression ratio (lower +proper optimization algorithms to deal with the new in value) results in higher computing cycles for task +objective functions and constraints caused by these new processing, thus increasing task computing latency and +metrics can also have a positive influence on system energy consumption. These tightly coupled tradeoffs of +performance. Recently, there has been a lot of research computing, communication, and accuracy make resource +on the new performance metrics of SemCom, such as allocation in SemCom inherently more complex. The +semantic similarity, semantic energy efficiency, and task detailed description about how the semantic compression +successrate,ofwhichwewillgiveadetaileddescription ratio influences latency and energy consumption is in +in Section III-C. Section III-A. +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2974 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +• Optimization with Non-differentiable and Implicit +Objectives: Many SemCom key performance metrics +rely on semantic similarity, which is difficult to express +analytically. These objectives often lack closed-form +expressions, are non-differentiable, or even implicitly +defined through closed-box models, which make +traditional optimization methods hard to apply well. +• Highly Coupled and Non-convex Optimization Variables: +Unlikeconventionalsystemswhereresourcevariablescan +often be decomposed or linearized, SemCom involves +complexcouplingbetweenvariablessuchascomputation +capacity, transmission power, and semantic compression +ratio. The resulting optimization problems are typi- +cally non-convex and nonlinear, in both objectives and +constraints. +• Task-related Semantic Information Transmission in Task- +oriented SemCom: In task-oriented SemCom systems, +the resource allocation is closely tied to the task-related +importance of the semantic information. For example, +tasks involving safety-critical or context-rich data trans- +mission (e.g., autonomous driving) may need to acquire Fig.5. Thetaxonomyofsystemframeworkestablishment. +high semantic fidelity, while other types of tasks may +tolerate coarse-grained transmission. This task depen- +dence necessitates adaptive resource allocation schemes to SemCom, followed by an examination of key network +thatalignwithtask-relatedsemanticinformationandtheir modelsusedinSemComresourceallocation.Wethendiveinto +utility, to complete the transmission of task-related and the core aspects of resource allocation within SemCom and +high semantic-importance features. At the same time, conclude with a preliminary review of the relevant literature, +it ensures the allocation of other resources (bandwidth, summarized in Table IV, which gives a preliminary summary +power, computing resources) to jointly optimize the of the literature on resource allocation in SemCom based on +overall system performance. source modal, communication mode, multiple access mode, +These challenges motivate the development of novel and resource allocation type. In the table, one(many)-to-many +optimization formulations and solution algorithms, as will be means one(many) BS(s)/edge server(s) to many users/end +discussed in the following sections. devices(EDs).Furthermore,weusethesymbol“–”toindicate +4) ResourcetoBeAllocatedinSemCom: Generallyspeak- that this property is not presented in the paper. +ing,thecurrentresearchonresourceallocationmainlyinvolves +computing, communication, and storage resources, with the +following resources typically requiring allocation. +III. PERFORMANCEMETRICSOFRESOURCEALLOCATION +• Computing resources: The computing frequency of Building upon the previous section, this section will review +CPUs/GPUs on the BS or user side, also known as the research on performance metrics in SemCom and the +computing capacity. formation of optimization objectives in different literature. +• Communication resources: The wireless resources used Usually, we evaluate a communication system based on +by BS or clients for data transmission, including band- its accuracy and effectiveness. The traditional communica- +width, power, etc. tion method is measured by the bit error rate and the bit +• Network parameter resources: The network-parameter transmission rate. For SemCom, accuracy can be measured +resources are the parameter settings in the SemCom by task performance and quantified by semantic similarity of +system, including the semantic compression ratio, the text transmission, character error rate of speech recognition, +neural network parameters, and other parameters or pol- etc. However, the efficiency of SemCom is usually difficult +icy settings. to measure and quantify [121]. As a result, it is critical +• Storageresources:EdgeserversorBSusethesehardware and challenging to establish new performance metrics for +storage resources to cache computing tasks and popular SemCom resource allocation. At present, the research of +content (such as road monitoring). SemCom resource allocation on constructing optimization +Inthispaper,wesummarizetheresourcestobeallocatedinthe objectives is mainly divided into two methods: based on +literature in Tables IX, XI, and XIII, and we need to mention traditional resource allocation performance metrics such as +thatthestorageresourcesareomittedsinceonlyonework[82] energy consumption, delay, and utility; and establishing new +considered them. The symbol “–” in the tables indicates that semantic-related performance metrics. See below for details. +this particular resource type is not allocated. Indifferentarticles,thesymbolsforthesamevariablesmay +In this section, we outline the foundational structure of the be inconsistent. To improve the reader’s understanding of the +SemCom resource allocation. It begins with an introduction compositionoftheseperformancemetrics,thispapermodifies +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2975 +TABLEIV +COMPARISONOFPAPERSFOCUSINGONDIFFERENTSOURCEMODAL,COMMUNICATIONMODES,ANDSCENARIOS +theexpressionsinsomeliteratureandunifiesthemathematical cell, etc. At this time, we follow the expressions in their work +expressions of common variables in different literature, as and provide additional descriptions. +shown in Table V. +In most of the literature, for the subscript of a single +A. Traditional Performance Metrics in Resource Allocation +variable, we use n to represent the n-th user, m to represent +them-thsubchannel,btorepresenttheindexofBS,xn,m =1 1) Energy Consumption and Time Delay: Energy con- +to represent the association of user n and subchannel m, and sumption and delay/latency are two of the most traditional +xn,m =0torepresentdisassociation.Insomeotherreferences, and commonly used performance metrics in resource +the subscript may refer to a task, a user group in a cellular allocation. +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2976 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +TABLEIV +(Continued.)COMPARISONOFPAPERSFOCUSINGONDIFFERENTSOURCEMODAL,COMMUNICATIONMODES,ANDSCENARIOS +VARIABL +T +E +A +S +B +D +LE +ES +V +CRIPTION +ρ is the compression ratio, fe is the computing capacity at +thetransmitter,andF(ρ,D) istherequiredcompressionCPU +cycles, which might be different across the literature. For +instance, [60] modeled F(ρ,D) as +αD +F(ρ,D)= , (15) +ρβ +where α > 0, β > 0 are constants relevant to the tasks. +Transmission latency is +T 2 = +ρD +, (16) +R +R is the transmission rate. Computing latency is +T 3 = +ρwDG +, (17) +fr +wherew istherequiredCPUcyclesperbittoprocessthetask, +For applications sensitive to delay, the design of a resource +fr is the allocated computing capacity at the receiver. We use +G todenotetheratioofcomputationintensityofsemanticdata +allocation algorithm to reduce latency is one of the main +to that of raw data. The increase is caused by computations +concerns [42], [58], [60], [73]. Delay modeling generally +forprocessingsemanticdataandcompensationsforenhancing +includes the following parts: a) semantic extraction latency +1 2 accuracy [59]. G can be denoted as +at the transmitter (T ); b) transmission latency (T ); and +c) semantic recovery latency or task process latency at the 1 +receiver (T 3 ). G = ρc , (18) +We previously mentioned in Section I-B3) that the +where c is a constant related to specific tasks. Fig. 6 shows +influence of the semantic compression ratio is the computing- the relation of compress ratio and G, where ρ min is the +transmission tradeoff in latency. For better understanding, we +minimum compression ratio to maintain the integrity of +simply model the latency of a single user in the semantic- +source information or task, which can vary from different +aware task process scenario, compression latency is +tasks/users/information modalities. +So the total latency is +T 1 = +F(ρ,D) +, (14) T =T 1 +T 2 +T 3 = +F(ρ,D) ++ +αD ++ +ρwDG +. (19) +fe fe ρβ fr +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2977 +2 +E )needstobeconsideredasaconstraintsincetheusersides +(like mobile devices) often have energy budgets. However, +there are some other scenarios like energy minimization +of energy efficient communication system, which needs to +consider the total energy consumption of both transmitter and +1 2 3 +receiver (E +E +E ). +To better show the relations of compression ratio and +latency/energy consumption, we illustrate them in Fig. 7a +and Fig. 7b, where +ρ∗ +t is the optimal compression ratio for +the minimum latency of single user and +ρ∗ +e is the optimal +compression ratio for the minimum energy consumption of +single user. In the figures, the range of compression ratio ρ is +in[ρ min ,1]duetotheρbelowthresholdρ mincannotmaintain +theintegrityofsourceinformationortask.ρ min canvaryfrom +different tasks/users/information modalities, here we set it to +0.5 for illustration, +In Fig. 7a and Fig. 7b, we can notice that the transmission +Fig.6. TherelationofG andcompressionratioρ. delay/energy decreases with decreasing compression ratio, +the computation delay/energy increases with the decrease +of compression ratio, and the optimal compression ratio to +It is obvious that compression ratio affect all parts of the total +reach the minimum value of latency and energy consumption +1 +latency,andcontrolsthetradeoffbetweencomputing(T and +is different, thus leading to a tradeoff in computing and +3 2 +T ) and transmission (T ). +transmission.(Note:Thisrelationmayvaryindifferentsystem +Whenthefocusofthearticleison“EnergyEfficiency”,the +models and with different users. The relation in Fig. 7 is an +totalenergyconsumptionoftheentiresystemisoftenusedasa +example illustration of a certain user.) +performance metric [50], [59], [62], [67], [87], [114]. In most +2) Utility Function: The concept of utility in resource +cases, delay and energy consumption are contradictory. The +allocation refers mainly to the satisfaction of users under +otheroftenbecomesaconstraintwhenoneistheoptimization +a certain resource allocation scheme. Utility is generally +goal. In [50], the authors proposed a semantic-aware energy- +expressed by the utility function. According to various objec- +saving task offloading network model. The goal is to extend +tives, the utility function is represented and mathematically +thebatterylifeoflocalusers,sothesumoflocalusers’energy +transformedbydifferentqualityofserviceparameters,suchas +consumption is used as the objective function. Considering +data transmission rate, delay, energy consumption, and cost, +the power shortage of mobile devices, the study in [59] is +which can achieve a better overall effect. The mathematical +committed to the allocation of resources for semantic-aware +transformation mainly includes reciprocal, logarithmic, and +MEC systems to minimize energy consumption. As discussed +weighted summation. Finally, an effective optimization algo- +in [67], the authors modeled the delay and total energy +rithm is designed to maximize the utility [46], [48], [49], +consumptionofasingleuserthatconsistsofthesethreeparts. +[117], [118]. For example, the utility function established in +The goal is to minimize the total energy consumption of the +the literature [117] is shown in Eq. (23): +entire system, considering constraints such as delay. +We also previously mentioned in Section I-B3) that the U =β 1A−β 2T −β 3E, (23) +influence of the semantic compression ratio is the computing- +whereAisthetotaltaskaccuracy,T isthetotaltimedelay,E +transmission tradeoff in energy consumption. Similarly, we +is the total energy consumption, and β 1 ,β 2 ,β 3 are the weight +also simply model the energy consumption in the semantic- +factors. +aware task process scenario. The energy consumption of +3) Traditional QoS and QoE: +semantic compression can be denoted as +• Quality of Service (QoS): Defined by the International +E 1 =κF(ρ,D)fe 2, (20) Telecommunication Union (ITU) as “the totality of char- +acteristicsofatelecommunicationsservicethatbearonits +where κ is a constant coefficient. F(ρ,D) also denotes the +abilitytosatisfythestatedandimpliedneedsoftheuser.” +CPU cycles required to compress the data D to ρD. The +It primarily focuses on system performance measured +transmission energy is +through physical parameters [133]. +E 2 =pT 2 =p +ρD +, (21) • Quality of Experience (QoE): Refers to users’ subjective +R perception of the system or service performance, influ- +where p is the transmission power. And the task computing enced by context, culture, expectations, psychological +energy can be denoted as factors, and more [133]. +E 3 =κ(ρwDG)fr 2. (22) In resource allocation for wireless communications, QoS +modeling is often similar to the utility function, but the +In many one-to-many uplink wireless communication scenar- mathematical complexity is higher than the general utility +t 1 +ios, only the transmitter’s energy consumption (E = E + function.In[47],theQoSmodelingbasedonthetransmission +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2978 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +Fig.7. Latencyandenergyconsumptionversussemanticcompressionratio. +delay and the number of received semantic information is manualsupervisionanddatareconstruction,suchastextsenti- +shown in Eq. (24). ment classification, image classification, and target detection, +semantic fidelity can be expressed as average classification +QoSm,n(t)= +accuracyordetectionaccuracy.Theestablishmentofmostnew +1 +(cid:10) (cid:11)(cid:10) (cid:11), performance metrics for SemCom resource allocation must +1+e β T (Tm,n(t)−T th ) 1+e β H Am(t)(H th −H˜ m,n(t)) rely on the concept of semantic similarity, so this section will +detail the current definition of various types of semantic +(24) +similarity. The comparison of different types of semantic +The two terms on the right side of the equation repre- similarities is presented in Table VI. +sent the transmission delay score and the received semantic 1) Semantic Similarity of Text Signal: For text transmis- +information score of the user n, respectively, where Tth and sion, BER does not reflect the performance well. In machine +Hth are the transmission delay thresholds and the received translation,bilingualevaluationunderstudy(BLEU)scoresare +semantic information and β T ,β H are the weight factors of generally used to measure results [136]. However, the BLEU +the delay in time and the received semantic information. score can only compare the differences between words in +The primary goal of wireless communication network twosentences,butcannotcomparetheirsemanticinformation. +services is to provide a user-satisfied quality of experience BLEU outputs a number between 0 and 1, representing the +(QoE) that is more user-centric. QoS does not contain any similaritybetweentwosentences,with1representingthehigh- +human-related quality factors, which means that for two estsimilarity.However,worderrorsmaynotalterthemeaning +different users, the same level of QoS may not guarantee of sentences. For example, the two sentences “That car had +the same level of QoE [134]. Designing QoE and managing been deserted” and “That vehicle had been abandoned” have +it while providing a service is necessary for high-quality the same meaning, but their BLEU scores are different due to +experiences. This requires assessment methodologies that can the use of different words to represent “car” and “deserted”, +quantifyQoE[135].Reference[71]studiedthetransmissionof which is a flaw in BLEU’s recognition of synonyms. A +imagesemanticinformationintheMetaverse3Dconstruction. word can have different meanings in different contexts. For +Data rate, bit error rate (BER) and interest score (the degree example,“bus”canhavedifferentmeaningsintermsofpublic +of interest in the image after semantic segmentation, which transportationandamicrocomputer.Traditionalmethods,such +is related to people) are considered when modeling QoE. In asword2vec[137],cannotrecognizeapolysemy.Theproblem +SectionIII-Cofthispaper,theQoEinthecontextofMetaverse is how to represent the word with a numerical vector, which +and SemCom is introduced. is different in different contexts [25]. +Therefore, based on the bidirectional encoder representa- +B. Semantic Similarity tion from transformers (BERT) model [138], Reference [25] +Semantic similarity is defined as the degree of similarity proposed a new metric, Sentence Similarity, which describes +between the sender and the receiver’s semantic information the similarity of two sentences according to their semantic +under a specific semantic task. For task-oriented SemCom, information, as shown in Eq. (25). +semantic similarity can be extended to semantic fidelity. +The specific representation of semantic fidelity varies with +ξ = +BΦ(s)·BΦ(ˆs) T +, (25) +different target tasks. For automated tasks that do not require (cid:3)BΦ(s)(cid:3)(cid:3)BΦ(ˆs)(cid:3) +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2979 +TABLEVI +COMPARISONBETWEENDIFFERENTSEMANTICSIMILARITIES +whereBΦ representstheBERTmodel.Thesentencesimilarity semantic information in the image is extracted into a scene +defined in Eq. (25) is a number between 0 and 1, which graph (SG) in the form of text, which captures the objects +representsthesimilaritybetweenthedecodedsentenceandthe andtheirrelationshipsintheoriginalimage.Thisinterpretable +transmitted sentence; 1 represents the highest similarity, and semantic information can not only be directly read and +0 represents no similarity. understood by humans but also be used to generate original +Currently, to measure text semantic similarity, most of images and retrieve similar images. +the literature [36], [52], [53], [55], [89], [90], [101] uses [42] introduced a comprehensive image-to-graph semantic +sentence similarity based on the BERT model as semantic similarity (ISS) metric, which uses a pre-trained deep neural +similarity. However, the authors of [40] proposed a metric of network (DNN) to directly capture the correlation between +semantic similarity (MSS), which is a function of semantic the original image and its semantic information without +accuracy and completeness. Based on token matching [139], any reconstruction of the image. The DNN is trained by +semantic accuracy is defined as the ratio of the sum of the Webimagetext [140],adatasetof400millionimage-textpairs +correct occurrences of each token in the recovered text to the collected from the Internet. Compared with the structural +sum of the occurrences of each token in the recovered text. similarity index measure (SSIM) [141], which measures the +Semantic completeness is defined as the ratio of the sum of difference between the original image and the reconstructed +the correct occurrences of each token in the recovered text to image on a set of pixels, the DNN can be used directly to +the sum of the occurrences of each token in the original text. obtaintheimagevectorandthesemanticinformationvectorof +Due to the high complexity of the expressions, we omit the thereceivedSG.TheISSmetricisdefinedasthecosineofthe +explicitexpressionofMSS.Reference[40]includesadetailed angle between the image vector and its normalized semantic +description of these metrics. triplet vector, which is calculated by the projection of the +2) Image-to-Graph Semantic Similarity: Although most of image vector on the set of semantic information vectors. The +the current work in the resource allocation of SemCom specific calculation steps and formulas are detailed in [42]. +is text and image modalities, the work of [58] and [42] 3) SemanticSimilarityofImageSignal: Thesemanticsim- +combines the two in semantic extraction and establishes an ilarity of the image signal is used to measure the similarity +image-to-text semantic information extraction method. The between the original image and the restored image. The +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2980 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +TABLEVII +MAPPINGBETWEENRESOURCETYPESANDSEMANTICPERFORMANCEMETRICS +more classical method is measured by the peak signal-to- C. New Performance Metrics for SemCom +noise ratio (PSNR), which is based on the errors between +As mentioned above, the traditional resource allocation +corresponding pixel points. In the previous section, SSIM +model is usually modeled based on Shannon capacity, +is mentioned. It is widely used in the application of image +which fails to give full play to the performance advan- +similarity measurement, including the resource allocation of +tages of SemCom to ensure the best performance of the +SemCom [47]. These two metrics are used mainly in the +SemCom network. SemCom does not require error-free trans- +imagesignalsimilarityevaluation.However,in[113],ametric +mission of bits or symbols, so the optimization problem +for image semantic transmission (MIST) is proposed, which +based on Shannon capacity construction may reduce system +combinestheimportanceweightofeachsemanticinformation +performance. Therefore, it is essential to reconsider resource +with its respective transmission quality to obtain the final +utilization from a semantic perspective to develop new +evaluationresults.Aftercapturingtheimage,theUAVsendsit +performance metrics [142]. +to the user and first extracts the semantic information through +Similarly, we will give a systematic summary and com- +O +the target detector. Specifically, a total of U objects are +parison of these new metrics in Table VIII, including a +detected, where i represents the i-th object and ci represents +critical evaluation of their strengths,limitations and suitabil- +its corresponding confidence. The relationship between the +ity for different modalities and applications. Considering +importance score Δi and the confidence ci of the object i can +that most of these new metrics are based on the con- +be expressed as Δi =c +i +σ , where σ is a variable that regulates +cept of semantic similarity, we illustrate the connections +the importance between different semantic information. The +and evolution of the semantic similarity-based metrics in +final MIST can be expressed as follows: +Fig. 8. +(cid:9)U +1) Semantic Transmission Rate and Semantic Spectral +E(A,Δi ,Q(pi))=A (Δi ×Q(pi)), (26) Efficiency: Firstly, reference [36] assumes that the semantic +i=1 unit (sut), representing the basic unit of semantic information, +where A represents the accuracy of extracting semantic canmeasuresemanticinformationinthetexttransmissionsce- +information, and Q(pi) represents the SSIM value of target nario. Then, two critical semantic-based performance metrics +i before and after transmission, which is a function that is are defined: semantic transmission rate (S-R) and semantic +positively correlated with the transmission power pi [43]. spectral efficiency (S-SE). +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2981 +Only SemCom that reaches the semantic similarity threshold +ξ th required by the downstream task is considered effective. +Let η n,m denote whether the user n performs an effective +semantic transmission on the subchannel m. If ξ n,m > ξ th, +then η n,m = 1; otherwise, η n,m = 0. Ψ is called ES-SE, +which can be expressed as: +(cid:9)N (cid:9)M +Ψ= xn,m η n,mΦn,m , (29) +n=1m=1 +where Φn,m is the S-SE of the user n in the subchannel m. +3) Task-Oriented S-R and S-SE: The authors of [109] +integrateS-RandS-SEintothescenariooffeatureimportance- +aware image classification, and two performance metrics of +the task-oriented SemCom system are defined: task-oriented +semantictransmissionrate(TOSR)andtask-orientedsemantic +spectral efficiency (TOSSE). Unlike the definition of [36], +whichconsiderslong-termtexttransmissionratherthansingle- +sentence transmission, the work of [109] focuses on the +performance ofeach user.Whenasemantictransmissiontime +slot begins, there are S semantic features after joint source- +channel coding (JSCC). BS obtains the feature transmission +Fig.8. Metricsthatbasedonsemanticsimilarity,theirconnections. ratedecisionvectorrf +basedonthechannelconditionsandthe +f +historical data distribution of each user. Then rn is fed back +tothefeatureselectionmoduleineachuserntodeterminethe +• S-R: S-R refers to the effective transmission of semantic number of features that need to be transmitted: Sn =rn f S/2. +information per second, measured by suts/s, as follows: +Therefore, the average semantic information for each symbol +Γn,m = WI ξ n,m , (27) of user n is In,m /Sn. +knL • TOSR: TOSR refers to the amount of semantic +allsubchannelbandwidthwasallocatedequally,usingW information effectively transmitted per second for a spe- +to represent the subchannel bandwidth. Since the article cific task. The expression is as follows: +focuses on long-term text transmission rather than the +WIn,m +transmission of a single sentence, I, L should take the ψ n,m = ξ n,m . (30) +Sn +expectedvalueandnottherandomvalue,thatis,foreach +user n, I/L is a fixed value, so omit the subscript n. The Compared to [36], it is equivalent to replacing I/knL +unitofI/knLissuts/symbol,andthechannelbandwidth (unit: sut/symbol) with In,m /Sn (unit: sut/symbol) of +of the band pass transmission in the ideal state is equal Eq. (27) in this paper, while the other parts remain +to the symbol rate (unit: symbol/s), so the unit becomes unchanged. +suts/s after multiplying by W. The semantic similarity • TOSSE: TOSSE refers to the rate at which task-related +based on BERT ξ n,m depends on the structure of the semantic information is successfully transmitted through +DeepSC neural network kn and the channel conditions a single bandwidth unit. The expression is as follows: +γ n,m. It can be expressed as ξ n,m =f(kn ,γ n,m). +ψ n,m In,m +• S-SE: S-SE refers to the rate at which semantic φ n,m = = ξ n,m . (31) +information is successfully transmitted within a unit W Sn +bandwidth, measured by suts/s·Hz, as follows: 4) Semantic Energy Efficiency: Based on the concept of +Γn,m I S-R [36], semantic energy efficiency (S-EE) is introduced +Φn,m = = ξ n,m , (28) in [88] as a measure of energy efficiency in the SemCom +W knL +system, which is quantified by suts/Joule. Traditional com- +The proposal of S-R and S-SE provides an impor- +munication systems define energy efficiency as the number +tant theoretical basis for many subsequent studies such +of bits that the system can transmit per unit of consumed +as [88], [90], [109]. Based on these two metrics, they made +energy. From a semantic point of view, the feature of S- +expansions and cross-domain transformations, and we now +EE is the number of semantic symbols transmitted by unit +continue with our discussion of them. +energy consumption. It is expressed as the S-R ratio that can +2) EffectiveS-SE: Thestudyin[90]consideredtherequire- +be achieved by the total power consumed in the SemCom +ment of semantic information similarity for downstream +network. The S-EE of user n is denoted by: +semantic tasks, and the concept of effective semantic spectral +efficiency (ES-SE) is introduced. The serious deviation of Γn wnI +semantic similarity will directly lead to inaccurate results. En = pn +pc = (pn +pc)knL ξ n , (32) +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2982 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +c +pn is the transmit power of user n, p is the electrical power it can be DeepSC-VQA [145]. In order to more reasonably +that the circuit consumed, and bn is the bandwidth. The Γn reflecttheuser’sQoErequirements,reference[22]established +here represents the S-R of user n. a semantic QoE model, which is expressed as: +(cid:9) +5) SemanticEntropy: Semanticinformationreliesnotonly +on the source data, but also on the specific task, which +QoEq b = wnGn R +(1−wn)Gn A +is significantly different from the information defined by n∈G n b +Shannon. Consequently, the same data may contain different (cid:9) wn (1−wn) += + . +amounts of semantic information for different tasks. In this 1+e βn(ϕr n eq−ϕn) 1+e λn(ξ n req−ξn) +n∈Gb +regard, the authors of [121] defined the semantic entropy as q +follows. (36) +Definition 1: GivensemanticsourceX,semanticentropyis +It should be noted that the authors of [22] modeled the +defined as the minimum average number of semantic symbols +complex situation of multi cell task and user. b denotes the +about data X ∈X that is sufficient to predict task Y, i.e., +H(X;Y) (cid:2)minE (cid:2) dim (cid:12) Code E S(X) (cid:13)(cid:3) ,ES ∈E S c In ell E i q n . d ( e 3 x 6) a , n G d q b q d d e e n n o o t t e e s s t t h h e e q in -t d h ex us o e f r t g h r e o u u s p er in gr t o h u e p b i - n th c c e e ll l s l . . +(cid:12) E S (cid:13) wn and (1 − wn) are the weights of the semantic rate ϕ n +s.t. P Y|Code E S(X) =P(Y|X), (33) and the semantic accuracy ξ n on the user n, respectively. Gn R +A +and Gn are the semantic rate and semantic accuracy for user +where Code E S(X)) denotes the semantic symbol vector n, respectively. β n and λ n represent the growth rates of Gn R +extracted from X with the semantic encoder ES, E S is the and Gn A . In addition, ϕr n eq and ξ n req represent the minimum +set of semantic encoders, and P(Y|X) is the conditional semantic rate and semantic accuracy of 50% scores [22]. +7) QoE of Metaverse Service Providers: With the support +probability of achieving the goal of Y given X. +ofvirtualreality(VR),augmentedreality(AR),andthetactile +The constraint in Definition 1 implies that the defined +Internet, Metaverse hardware devices cannot only mobilize +semanticentropyislosslessandthatitisactuallydefinedasan +expectedvaluethroughoutthedatasetX,thatis,thesemantic all senses of the user and provide an immersive experi- +ence [146], but also revolutionize the way people interact +entropy is constant for the same task and dataset. However, +∗ with each other and even with objects. Therefore, it is crucial +it is intractable to find an optimal semantic encoder, E , to +S +to design the QoE of Metaverse Service Providers (MSPs) +derive the semantic entropy [143]. To obtain a measure that is +as a performance indicator to measure the performance of +bothmeaningfulandmanipulableforsemanticcommunication +Metaverse Service [147]. In the proposed framework in refer- +systems, [121] utilize a well-designed DL model as the +ence[71],theauthorsaimtotransmitthesemanticinformation +encoder to obtain an approximate semantic entropy for a task, +of interest to each MSP. Therefore, the performance metrics +which is: +(cid:2) (cid:12) (cid:13)(cid:3) of the data rate, the BER, and the interest rating should be +H(X;Y) (cid:2)minE dim Code E DL(X) considered together. Thus, the QoE of the k-th MSP Uk can +(cid:12) (cid:13) +be defined as [148]: +s.t. P(Y|X)−P Y|Code E DL(X) <ε, (34) +(cid:9)N +k +(cid:12) (cid:13) +where the constraint indicates that the task performance Qk = J k iT 1−B k i , (37) +degradation can not exceed ε. From Eq. (34), the defined i=1 +a +af +p +o +p +r +r +e +o +m +xi +e +m +nt +a +i +t +o +e +ne +s +d +em +m +a +e +n +th +ti +o +c +d, +e +t +n +h +t +e +ro +a +p +p +y +pr +i +o +s +xim +lo +a +ss +te +y. +se +A +m +c +a +c +n +o +t +r +i +d +c +in +e +g +ntro +to +py +th +o +e +f +where Nk is the number of objects that Uk is interested, J +k +i +is the normalized interest rating of Uk for the i-th object +the considered tasks can be derived based the corresponding +recommended to Uk, T is the normalized time that all MSPs +DLmodels.Therefore,[121]usesemanticentropytoconstruct +finish the transmission, and +Bi +is the BER of transmitting the +the semantic rate and semantic QoE model. We now move on k +i-th object’s semantic information to Uk. +to this semantic entropy-based metric - semantic QoE. +8) System Throughput in Message: System throughput in +6) Semantic QoE: The accuracy and efficiency of message +message (STM) represents the network performance from +transmission are different from the user’s point of view, and +a semantic point of view, proposed by [97]. In text com- +depending on the application, users may have their own +munication, an entire text sentence ending in a cycle, or +preferences for them. For example, some users prefer higher +in voice communication, a completely emitted voice signal, +accuracy but have a certain tolerance for delay, while some +can be regarded as a message. Taking this into account, the +users may want to get a higher rate but do not need high +message rate (unit: msg/s) is interpreted as the number of +accuracy [144]. The semantic rate of user based on semantic +messages transmitted or processed per unit time under the +entropy is given as +reference of the bit rate (unit: bit/s) definition. Because the +ϕ n = k H n ˜ / D W L , (35) s fr y a s m te e m wo th r r k ou b g as h e p d ut o h n a S s h a a v n e n r o y n p ’s er t f h e e c o t r e y x : pressionofthesystem +(cid:9)(cid:9) (cid:9)(cid:9) +where the meaning of W and kn is the same as in Table V. S T = xnbrnb = xnbwnblog2(1+γ nb). +H˜ DL isthesemanticentropybasedonspecificDLmodel.For n b n b +text modal task, it can be DeepSC [25]. For bi-modal task, (38) +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2983 +Here, n and b represent the n-th user and the b-th BS, respec- The first attribute is usually represented by a non-decreasing +tively. Among them, wnb and γ nb represent the bandwidth time penalty function f(t) ∈ T, which includes metrics like +and SNR, respectively. The system throughput represents AoI. The second attribute is captured by an error detection +the number of bits successfully transmitted per unit time function g(Xt ,Xˆ t)∈X, typically encompassing metrics such +in the system, reflecting the network performance. Therefore, asmeansquareerror(MSE)ormeanpercentageerror(MPE). +the authors of [97] defined a general bit-to-message (B2M) The third comes from practical constraints like spectrum limit +conversion function S(·), which is related to different seman- and energy consumption, denoted by a predefined function +tic encoders, knowledge matching, and message properties. C(Xt ,dt)∈C basedonsourcestatesXt andactiondt,where +Therefore, according to the bit rate rnb given by the Shannon the latter refers to the transmission policies like generation +M +theorem, the message rate r +nb += Sn(rnb) can be naturally decisions, code rate, and resource allocation. +defined by S(·), and the expression of STM is derived as 11) Efficiency of Semantic Information: In the context +follows: of the SemCom-Industrial Internet of Things (SemCom- +(cid:9)(cid:9) (cid:9)(cid:9) +IIoT), traditional performance metrics are no longer the best +S TM = xnbrn M b = xnbSn(rnb). (39) choice. As reported in [108], a new performance metric was +n b n b +designed at the semantic level, named Efficiency of Semantic +STMcharacterizesthenumberofmessagessuccessfullytrans- Information(EoSI).Thescenarioisrelativelydifferent,andwe +mittedinthesystemperunittime,whichcanwellcharacterize needtostatethatn,mdoesnotrefertotheuserandsubchannel +network performance from a semantic perspective. indexes only here. The intelligent sensing device (ISD) in +9) Age of Semantic Information: In traditional communi- the scene is divided into m categories, so the subscript of +cation systems, Age of Information (AoI) [149] is a popular ISDm,n meansthen-thISDinthem-thclass.Thepreliminary +measure of information importance, which is defined as expression of EoSI is as follows: +Δ AoI (t) = t − u(t) by measuring the information delay +UoSIm,n(t) +of the destination. u(t) is the generation time of the latest EoSIm,n(t)= . (44) +costm,n(t) +received data packet. In order to capture the freshness of +information and semantic loss in the SemCom system, the lit- UoSI is semantic information utility: considering both seman- +erature [89] proposed a new measurement method called Age tic timeliness and task accuracy, the expression is as follows: +ofSemanticImportance(AoSI).Beforegivingthedefinitionof +AoSI, the reference [89] first defined the semantic importance +UoSIm,n(t)=Fm d ,n(t)Fm a ,n(t). (45) +(SI): semantic loss caused by missing or incorrect semantic Among them, task accuracy Fm a ,n(t) quantifies the impact +content [150]. It can be expressed as ψ = 1−ξ. Here, ξ is d +of semantic information on task accuracy, Fm,n(t) quantifies +the semantic similarity, which we discussed in the previous +the impact of the timeliness of semantic information on the +subsection. For example, in a text transmission task, semantic +timeliness of task results, and the timeliness of task results is +importance can be denoted as +also the standard for judging whether the task is successfully +B(x)·B(xˆ) T completed. costm,n(t) represents the resource overhead of +ψ =1−ξ =1− +(cid:3)B(x)(cid:3)·(cid:3)B(xˆ)(cid:3), +(40) ISDm,n to complete intelligent tasks, which is a weighting +function of bandwidth resources, local computing resources, +where B(·) represents the BERT model. The definition of and MEC computing resources. The complete expression of +AoSI can be obtained by the definition of SI and AoI: EoSI is complex. If you are interested in the details and the +Δ AoSI (t)=Δ AoI (t)·ψ(u(t))=(t −u(t))·ψ(u(t)),(41) derivation process, see [108]. +12) Success Probability of Tasks: In order to simulta- +where ψ(u(t)) is the semantic importance of the last received neously evaluate the impact of transmission and adaptive +semanticcompression(ASC)ontheperformanceofSemCom, +packet. +a new performance metric is defined in [106]: success prob- +10) Utility of Information: Reference [72] introduced a +ability of tasks. Reference [111] further improved the work +utility of information (UoI) metric. It encompasses multiple +in reference [106] and also adapted this performance metric. +contextualattributestocapturetheutilitygradeoftheupdates +According to [106], the definition of success transmission +transmitted to communication systems or services. From a +probability of users is first introduced, as follows: +mathematicalperspective,itcanbemodeledusingacomposite +(cid:6) (cid:7) +function: +2 +an(1−on)−1 +P(tn ≤t0)=2Q , (46) +U(t)=(Θ◦U)(D t). (42) bn δ +Here, Θ(·) : Rm → [0,M] is a non-increasing function where tn is the transmission delay of user n, P(·) is the +that converts the penalty into the corresponding utility grade. probability, and on is the semantic compression ratio. In +U :Rn →Rm,n ≥m,isanon-decreasingnon-linearpenalty practical scenarios, such as the Internet of Vehicles (IoV), +function with respect to the three attributes as follows: a large number of tasks are delay sensitive, so there are +(cid:2) (cid:3) always strict transmission delay constraints, represented by +f(t),g(Xt ,X ˆ +t +),C(Xt ,dt ) ∈T ×X ×C → F U(D +t +)∈Rm. +t0. Therefore, the user’s transmission success probability is +(43) P(tn ≤ t0). an = w d n 0 t 0 , wn is the bandwidth of user n, +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2984 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +• Semantic Fidelity: Defined as the fidelity between the +original vectorized data X and the received information +Xˆ. It is expressed as: +(cid:12) (cid:13) (cid:12) (cid:13) +SF ε,n X,Xˆ =fsa X,Xˆ , (49) +where the subscript n represents the vehicle n, ε repre- +sentstheindexoftheedgeserver,andfsa(·)isthefidelity +mapping function, which varies with the task. +• Semantic Timeliness: Semantics will evolve over time. +By modeling and tracking temporal changes, includ- +ing aggregating new semantic information as much as +possible, communication efficiency can be significantly +improved,andtheprobabilityoferrorsinsemantictrans- +mission can be reduced. The timeliness of the semantic +information extracted by the system is defined as: +(cid:14) (cid:15) +ST ε,n(·)=fst,ς +Tth −T +, (50) +Fig.9. Therelationoftaskaccuracyandsemanticcompressionratio. Tth +where fst,ς(·) is a non-linear decreasing function with +parameter ς on semantic timeliness. T is the total delay +and d0 is the initial extraction of semantic information for +users without semantic compression. bn = +N +p +0 +n +Bn +, pn is the +t +o +h +f +e +th +to +e +ta +sy +l +s +d +t +e +e +l +m +ay +, +, +a +t +n +h +d +e +T +g +t +r +h +ea +i +t +s +er +th +t +e +he +de +s +l +e +a +m +y +a +c +n +o +ti +n +c +st +t +r +i +a +m +in +e +t +l +. +in +T +e +h +s +e +s. +lower +transmissionpoweroftheusern,andN0isthespectraldensity +of the noise power. +δ2 +is the variance of channel gain. The Q +The following formula defines semantic utility: +function represents the tail distribution function of a standard Qa n ll =ζ n SF ε,n +χ n ST ε,n . (51) +normal distribution. So we can obtain the n-th user’s success +probability of tasks: Among them, ζ n and χ n are the preferences of semantic +fidelity and semantic timeliness, respectively. +Ωn =η(on)×P(tn ≤t0), (47) 15) SemComQoS: Semanticsimilarityisfurtherpromoted +by [53], and SemCom QoS (SC-QoS) based on Semantic +where η(on) is the probability of which task is success- +Quantization Efficiency (SQE) is created as follows: +fully executed under successful transmission. It can be seen +• SQE: In order to solve the tradeoff between semantic +from (47) that the task success probability proposed by [106] +accuracyandthenumberofbitsconsumed,anewmetric, +for evaluating SemCom performance can control the tradeoff +SQE, is proposed. This metric quantifies the ratio of the +between semantic transmission and semantic understanding. +semantic similarity gain of each semantic feature to the +13) Transmission Efficiency of Tasks: In [112], the authors +bit-relatedsemanticsimilaritygain.Duetoitsstrongcor- +modeled the physical channel as a non-trainable fully con- +relation with the novel semantic bit quantization (SBQ) +nected layer to simulate different channel states. With the +proposedintheirwork,thesecontentsarenotintroduced. +help of the curve fitting method, the mathematical relation- +See [53] for more details. +ship between compression ratio and task performance under +• SC-QoS: Defined based on SQE and transmission delay, +different channel states is explored. Then a new measurement +and the effective SC-QoS is expressed as: +standard is established in [112]: transmission efficiency of +(cid:9)N (cid:12) (cid:13) +tas T k h s. e transmission efficiency of the task is defined as the Ψ= (cid:19)(cid:16) n (cid:11) −φ G G(cid:16) n , (52) +n=1 +weighted sum of the number of packets from each user and +the corresponding achievable task accuracy at the receiver. where the user’s index is n, (cid:19)(cid:16) n (cid:11) is the effective SQE +Specifically, the semantic task transmission efficiency vt in (the sum of SQE whose semantic similarity satisfies the +time slot t is defined as follows: minimum threshold), G(cid:16) n is the delay, and φ G is the +(cid:9)J (cid:9)N j balance coefficient. +vt = vt n,j ×A n t ,j. (48) 16) Semantic Score: To measure the overall semantic loss +betweentheoriginalsentences andthereconstructedsentence +j=1n=1 +ˆs atthereceiver,theworkin[151]definesanewmetricnamed +Thesubscriptndenotesusernandjdenotestheintelligenttask SemanticScore(SS).whichcombinesthebestoftwodifferent +n,j +j corresponding to user n. At is the classification accuracy quantities, BLEU score and sentence similarity which uses +n,j +and vt is the number of data packets that each user can BERT. The BLEU score cannot handle word synonyms, but it +transmit in slot t. is a fast and low-cost algorithm that is language independent +14) Semantic Utility: The reference [107] proposed a and corresponds to human judgment. The sentence similarity +semantic utility measurement method that considers semantic score using BERT vectors is slow and has ratings comparable +timeliness and semantic fidelity. to the BLEU, but it also handles synonyms. Let Δλ(s,ˆs) +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2985 +denote the SS between sentence s and ˆs, which is a convex +combination of BLEU(s,ˆs) and ξ(s,ˆs). +Δλ(s;ˆs)=(1−λ)BLEU(s,ˆs)+λξ(s,ˆs), (53) +where λ∈[0,1] is a parameter. +In this section, we explore the construction of the objective +function in resource allocation of SemCom, which is a key +to the modeling of optimization problems. We provide a +detailed review of performance metrics, categorizing them +into two types. The first type includes traditional metrics +such as delay and energy consumption, while the second type +focuses on new metrics based on semantic similarity. We give +two comprehensive comparative matrices to better synthesize Fig. 10. The taxonomy of centralized resource allocation algorithms in +findings across references. To further clarify the influence of SemCom. +different resource types on these performance metrics, we +provide a resource–metric mapping summary in Table VII, +(SCA) methods, and the interior point method, as well as +in which we use some clear examples in different studies to +some other mathematical algorithms based on other mathe- +illustrate this influence. +maticalalgorithms,suchastheHungarianalgorithm[153].An +optimizationalgorithmbasedonconvexoptimizationtypically +IV. CENTRALIZEDRESOURCEALLOCATIONALGORITHMS +combines several of these techniques. +In order to realize resource allocation in SemCom and +1) Lyapunov Optimization: Lyapunov optimization is a +meet the requirements of these performance metrics proposed +powerful long-term resource optimization scheme to find +above, advanced resource allocation strategies and algo- +stability or equilibrium points of dynamical systems with +rithms are essential. The optimization problem constructed +stochastic properties of nonlinear systems. It requires less +is extremely complex and differs significantly from the tra- +priorknowledgeandhaslowcomputationalcomplexity[154]. +ditional communication architecture in terms of objectives, +Lyapunov optimization focuses on analyzing and optimizing +constraints, and optimization variables. It is a challenge to +stochasticnetworks(networkscharacterizedbyrandomevents, +construct a well-performing optimization algorithm that can +time-varying dynamics, and uncertainties). It is particularly +adapt well to SemCom. Currently, there are a variety of +well-suited for applications in communication systems and +centralized algorithms for resource allocation in SemCom, +queueingsystems.Theauthorsof[102]adoptedtheLyapunov +mainlyconsistingofconvexoptimization,heuristicalgorithms, +optimization method to solve the problem, which first trans- +andDRL.Fig.10showsthetaxonomyofcentralizedresource +formsthelong-termconstraintsintoqueuestabilityconditions +allocation algorithms in SemCom. +using the concept of virtual queue and then transforms the +In recent years, many researchers have summarized the +long-termobjectivefunctionandthequeuestabilityconditions +state-of-the-art resource allocation algorithms of various sce- +intosolvableshort-termsubproblems.Similarly,[59]and[118] +narios in their surveys. In [16], the authors summarized +also used Lyapunov optimization techniques to transform +different optimization methods for resource allocation in edge +the original stochastic optimization problem of multiple time +computing. The comparison tables of different papers are +slots into a series of deterministic problems in a single +designed according to the objective, brief description of +time slot. Lyapunov optimization, as a stochastic optimization +the methods, advantages, and disadvantages. Reference [152] +method, enables online decision making while maintaining +summarized different resource allocation schemes for the +sub-optimal performance. Therefore, it applies well in a +two dominant vehicular network technologies, e.g., Dedicated +long-term stochastic scenario in SemCom system, like the +Short Range Communications (DSRC) and cellular-based +semantic-aware dynamic long-term MEC systems using time +vehicular networks. In this subsection, centralized resource +division duplexing (TDD) in [59]. Lyapunov optimization can +allocation optimization algorithms from different literature in +also combine with DRL-based method, in [72], expanding on +SemCom are reviewed. +the Lyapunov transformation, the UoI minimization problem +is converted into a sequence of deterministic single time-slot +A. Algorithms Based on Convex Optimization and optimization problems. Subsequently, the DRL-based method +Mathematical Techniques PPO(willbeintroducedlaterinSectionIV-B)isusedtotackle +Because resource allocation involves a lot of variables this problem. +and constraints, the corresponding optimization problems are 2) Alternating Optimization Algorithm: The alternating +usuallycomplex,evennon-convexorNP-hard.Aconsiderable optimization(AO)algorithmistodecomposetheoptimization +part of the research transforms the non-convex problems into problem into several sub-problems, and then these sub- +near-convex or convex optimization problems, which leads problems are solved iteratively. Commonly used in the case +to feasible convex optimization methods. The main tech- of multi-variable optimization, which iteratively optimizes +niques include Lyapunov optimization techniques, alternating each variable while treating other variables as a fixed value. +optimization (AO) algorithms, successive convex approximate Depending on the specific problem, the complexity of the +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2986 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +TABLEVIII +COMPARISONBETWEENDIFFERENTNEWSEMANTICMETRICS +decomposed problem varies; the simpler case is decom- extraction strategy subproblem and the wireless resource +posed into two to three subproblems, where each subproblem allocationsubproblem,whichwillbeoptimizedalternatelyand +optimizes a single variable in [44], [45], [57], [91], [98], iteratively, where each of the two subproblems also employs +[119], [130], [132]. As the problem and the optimization the AO algorithm to optimize the corresponding parameters. +variables increase, the optimization problem is decomposed Aniterationofthealgorithmforthetotaloptimizationproblem +into three subproblems in which the subproblem has two or contains the number of iterations L1 and L2 of the AO +more optimization variables in [59], [66], [67], [74], [93], algorithm for the two sub-problems. +[100], [105]. A more complicated situation occurs in [68], 3) Successive Convex Approximate: The idea behind suc- +where the paper employs a nested AO algorithm to divide cessive convex approximate (SCA) istofindalocallyoptimal +the optimization problem into two subproblems: the semantic solution to the original problem by iteratively solving a series +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2987 +Algorithm 1 Basic SCA Algorithm for Problem P maximummatchingofabipartitegraphbasedonanymatching +Find a feasible solution x ∈ X in P, choose a step size ifwehaveawaytokeepsearchingforaugmentingpathsuntil +θ ∈(0,1] and set k = 0. eventuallywefindnonewaugmentingpaths.Thecoreideaof +Repeat theHungarianalgorithmistoiterativelysearchforaugmenting +1) Compute xˆ(xk ), the solution of P xk; paths to get a maximum match. +2) Set +xk+1 =xk +θ(xˆ(xk )−)xk +; The Hungarian algorithm can solve the allocation problem +3) Set k ←k +1 in polynomial time, which can significantly reduce the algo- +Until convergence criterion is met. rithmic complexity. When it comes to the scenario of the +resource allocation problem in SemCom, it is usually used +for the subproblem of subcarrier pairing/subchannel alloca- +of convex optimization problems similar to the original non- tion after the original optimization problem is decomposed +convex problem. Consider the following optimization: by the AO algorithm above. In the literature [36], [109], +and [57], the optimization subproblem of channel allocation +P:min U(x) (54) is regarded as a bipartite graph matching problem, and then +x +the Hungarian algorithm is used to solve this optimization +s.t. gl(x)≤0, ∀l =1,...,m (54a) subproblem. Among them, the knowledge-assisted proximal +x∈K (54b) policy optimization (K-PPO) algorithm is proposed in [109], +which uses the Hungarian method to determine channel +where the objective function and constraint (54a) is smooth allocation, greatly reducing the complexity of the original +(possibly nonconvex), the feasible set is denoted as X. The proximal policy optimization (PPO) algorithm by introducing +original non-convex or non-concave function is transformed the Hungarian algorithm. The details of PPO will be intro- +into a series of convex or concave functions. The convex duced later in Section IV-B. +approximationoftheoriginalproblemcanbestatedasfollows: 5) Lagrange Methods: The Lagrange multiplier method +given xk ∈X: is a common method for solving constrained optimization +(cid:12) (cid:13) +problems. For the optimization problem with only equation +P xk:m x in U˜ x;xk (55) constraints, you can directly use the Lagrange multiplier +method to list the Lagrange function, which will be trans- +(cid:12) (cid:13) formed into an unconstrained optimization problem to solve. +s.t. g˜l x;xk ≤0, ∀l =1,...,m (55a) Fortheoptimizationproblemwithinequalityconstraints,using +x∈K (55b) the Lagrange function to optimize it must satisfy the Karush- +Kuhn-Tucker(KKT)condition,whichisanecessarycondition +whereU˜(x;xk )andg˜l(x;xk +)representtheapproximationsof for taking the optimal parameter values and a sufficient +U(x)andgl(x)atcurrentiterationxk +,respectively,thefeasible condition for some special convex optimization problems. +set is denoted as +X(xk +). We can summarize the basic SCA Problems containing inequality constraints after listing the +algorithm in Algorithm 1. Lagrangian function still have constraints that are not easy to +This process is repeated until the stopping criterion is deal with, then it can be transformed into a Lagrangian dual +satisfied. It is assumed that at each iteration, some original problem; this dual problem must be a convex optimization +functions are approximated by their upper bounds, where the problem and therefore easy to solve. But in order to make +same first-order behavior is preserved [155]. the dual problem and the original problem have the same +Since an approximate solution to the original optimization solution, it must satisfy the strong duality. The sufficient +problem is solved in each iteration, there is no condition is Slater’s condition; the necessary condition is the +guarantee that the global optimum will be obtained. KKT condition. Lagrangian methods have been employed +The convergence of the method is guaranteed due to in many works, where the problem is decomposed into +convexity/concavity [19]. subproblems and then the sub-optimization problem is solved +AO algorithms and the SCA algorithm are two methods using Lagrangian methods [57], [68], [102], or the problem +that work well with each other, and almost all the literature is transformed directly using the Lagrangian methods to +on SCA uses a combination of the two. Decomposing a solve [78]. +largenon-convexoptimizationproblemintoseveralsmallnon- Summary: Traditional optimization algorithms based on +convex optimization subproblems to solve iteratively reduces convex optimization techniques and other mathematical +the difficulty/complexity of the SCA algorithm, thus allowing algorithms are applicable to small-scale solutions and high- +the difficulty and complexity of the overall problem to be reliability demand scenarios. They have the following +reduced[44],[45],[49],[59],[67],[68],[70],[91],[92],[106], advantages: a) mature and widely used; b) easy to obtain sub- +[111], [119]. optimal optimization results; c) not relying on data. However, +4) Hungarian Algorithm: The solution to the maximum algorithms based on these techniques are often too complex. +matching problem in bipartite graphs is the origin of the As a result, its complexity makes it difficult to implement in +Hungarian algorithm. Since a maximum matching of a bipar- practical systems and not suitable for large-scale problems. +tite graph necessarily exists, e.g., the upper bound is a perfect Although algorithm complexity may vary due to different +matching that contains all vertices, it is possible to get a problems and scenarios, we can still give a brief summary +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2988 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +of these algorithms. In terms of computational complexity, With the development of DL and reinforcement learning +Lyapunovoptimizationitselftypicallyhasthelowestcomplex- (RL)techniques,puredata-drivenDRLhasbecomeapowerful +ityduetoitsonlineanddynamicnature,makingitsuitablefor tooltosolvecomplexresourcemanagementproblemsinrecent +real-time systems. The complexity of Lyapunov optimization years [81], [156], [157]. By efficiently learning the dynamics +is primarily determined by the per-slot deterministic sub- of the environment, DRL can provide resource allocation +2 3 +problem, and often falls in the range of O(n ) to O(n ) strategiesthatmaximizelong-termreturnsbasedonpretrained +when convex formulations are involved, making it particu- policy networks. +larly suitable for low-latency real-time systems. Alternating RL and DRL approaches can be mainly distributed in two +optimization (AO) and Lagrangian methods exhibit moderate ways:basedonvaluefunctions(1-4)andbasedonpolicygra- +complexity, with AO being effective for decomposable non- dients(5-9).Thispaperalsoprovidesabriefdescriptionofthe +convex problems and Lagrangian methods for constrained algorithms based on these techniques in various publications. +optimization. The complexity of AO is mainly determined by 1) Q-Learning: Q-Learning (QL) [158] is an off-policy +the complexity of solving each subproblem. For instance, if control method for finding the optimal policy, mainly used in +each subproblem involves convex optimization with complex- discrete action space. The core idea is to utilize a Q function +3 +ity O(n ), and k such subproblems are solved per iteration, that represents the expected reward of taking an action in +thetotalcomplexityperiterationbecomesapproximatelyO(k· a particular state. The Q function updating rule satisfies the +3 +n ). For Lagrangian-based methods, the overall complexity Bellman equation: +depends on both the structure of the primal problem and the (cid:17) (cid:18) +(cid:2) (cid:3) +methodusedforupdatingdualvariables.Iftheprimalproblem Q(s,a)←Q(s,a)+α r +γmaxQ s (cid:4),a (cid:4) −Q(s,a) . +admitsaclosed-formsolution,eachiterationmayinvolveonly a(cid:2) +2 +dual updates with complexity around O(n ), leading to a (56) +total complexity of O(K · n 2 ), where K is the number of +iterations. However, if the primal problem requires solving In [71], the selection of public messages uses QL techniques. +a numerical optimization (e.g., quadratic programming), the The work of [124] compares the QL-based approach with +per-iteration cost may increase to O(n 3 ), resulting in a total the convex optimization-based approach under the video +complexity of O(K ·n 3 ). Successive convex approximation semantics-drivenresourceallocationproblem.Theexperimen- +(SCA)tendstohavehighercomplexityduetoiterativeconvex tal results prove that the QL-based approach performs better +approximations. Each subproblem often requires O(n 3 ) time, than the convex optimization-based approach. +and the total complexity O(T ·n 3 ) grows linearly with the 2) DeepQNetwork: Mnihetal.[159]introducedthedeep +number of iterations T. Thus, SCA is suitable for non- Qnetwork(DQN),whichpioneered thefieldofDRL.Inreal- +convex problems with a manageable size and structure. The world scenarios, the number of states can be large, making +3 +Hungarianalgorithm,withacomplexityofO(n ),isefficient the construction of Q-tables computationally intractable. To +for small-scale linear assignment problems but less scalable addressthislimitation,DQNusesaneuralnetworktoestimate +for larger systems. Table IX reviews the literature using the Q-values of each state-action pair. The most important +these traditional optimization techniques, which are based feature of DQN is that it uses experience replay [160] +on convex optimization techniques and other mathematical and target networks to stabilize the training of deep neural +algorithms. networks [161]. As mentioned in the previous paper, [89] +defined the AoSI metric. In the paper, the long-term average +AoSI optimization problem is modeled as an MDP, and a +DQN-based algorithm is proposed to find the suboptimal +B. Algorithms Based on Deep Reinforcement Learning solution for source scheduling and the number of semantic +In the context of SemCom, direct modeling of the relation- symbols.Comparedwiththesimplerstatespacecasemodeled +ship between semantic accuracy (or fidelity) and optimization intheliterature[71]usingQL,mostoftheresourceallocation +variables, such as the semantic compression ratio, is often problems in SemCom have a more complex state space, so +infeasibleduetotheabsenceofexplicitanalyticalexpressions. DQNisobviouslymoresuitable.In[22],theexhaustivesearch +Then results in the non-differentiable and implicit objectives. to solve the semantic compression subproblem will lead to +To address this challenge, some authors [112], [121] use high computational complexity. Because when using exhaus- +different curve fitting techniques to approximate this implicit tivesearchtosolvethiscombinationoptimizationproblem(for +relationship. For instance, in [121], neural networks are K-users,thereareK!permutations),thecomplexitywillgrow +adopted to fit the relationship curve of semantic fidelity and exponentially with the number of users and cells. Therefore, +optimization variables (power, channel assignment, semantic in the journal version of [22], that is, the reference [121], the +compression)foreachtask(singlemodalandbi-modal).Once authorsproposedasolutionthatcombinesDQNandmatching +this approximation is obtained, the originally implicit objec- theory. The exhaustive search is replaced by the DQN-based +tive becomes differentiable or at least numerically tractable. method to improve overall QoE effectively. +However, after curve fitting, the output fitting function is still 3) Double Deep Q Network: Hasselt et al. [162] proposed +complex and non-convex. Traditional mathematical methods the double deep Q network (DDQN) to solve the over- +are often difficult to model or calculate in the face of these estimation problem in QL. The DDQN algorithm borrows +complexities. from the double-Q learning algorithm [163] and makes +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2989 +TABLEIX +CENTRALIZEDALGORITHMBASEDONCONVEXOPTIMIZATIONMETHODANDMATHEMATICALTECHNIQUES +improvements to the DQN algorithm: estimating the pol- went one step further than the QL-based work [124] +icy based on the online Q-network, selecting the action, that was already mentioned. They wanted to improve the +and estimating the Q-value with the target network. Some accuracy of video semantic understanding and build a +experimental results show that DDQN finds a better strat- multidimensional resource allocation model that combined +egy than DQN in Atari games. The authors of [82] communication, computation, and caching. They designed +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2990 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +the DDQN-based algorithm, which is shown to achieve bet- have also been attempts in the literature to use TD3 instead +ter results than those achieved by the QL-based approach of DDPG as the base algorithm of the scheme [107], [108], +in [124]. in which [107] proposed a TD3-driven dynamic semantic- +4) DuelingDoubleDeepQNetwork: Duelingdoubledeep aware algorithm: dynamic semantic-aware TD3 (DSATD3) +Q network (D3QN) is a combination of Dueling DQN [164] for a federated learning-driven semantic vehicular network +and DDQN. Dueling DQN separates the computation of to guide agents in adopting accurate semantic extraction and +Q-values into two components: the value function (V) and resource allocation strategies. The simulation results showed +the advantage function (A), which enables Dueling DQN to that DSATD3 has better performance compared to DDPG- +provide more accurate Q-value estimation while needing less based approaches. +discrete action data, thus improving sample efficiency. As in In contrast to the previous two papers, the work in [75] +Table X, the authors of [90] and [94] both use D3QN for improves the TD3 algorithm and proposes the TD3-RNS +discrete action in the whole DRL framework. algorithm (TD3 with reference neuron-enhanced Softmax) to +5) Actor-Critic: The actor-critic (AC) [165] algorithm solvealong-termsemanticthroughputmaximizationproblem. +learns both the policy and the state-value function, using the The actor network uses a reference neuron technique and a +value function to reduce variance in policy updates. Actor- linearly decreasing Gaussian action noise in the output layer +criticmethodstendtobemorestablethanpurepolicygradient to enhance training efficiency and balance exploration and +methods. In the work of [58], the allocation of transmitted utilization by the agent. +semantic information and resource block (RB) was jointly 8) Proximal Policy Optimization: Proximal Policy +optimized to minimize the average transmission delay, based Optimization (PPO) is proposed by Schulman et al. [169] +ontheimprovedACalgorithm,inwhichanovelvaluefunction in 2017. PPO aims to improve and simplify previous policy +is designed to improve the probability of action exploration gradientalgorithms,suchasTrustRegionPolicyOptimization +and finding the optimal solution. In traditional model-free (TRPO).ThekeyaspectofthePPOalgorithmisthatitmakes +DRL, the value function V(s k+1) is approximated by DNN: the learning process more stable by limiting the magnitude +E s ∼P[V(sk+1)].Inthemodel-basedDRLproposedinthe of policy updates. The authors of [71] designed a power +k+1 +article, due to the deterministic nature of the state transitions, allocation algorithm to maximize the total QoE based on +there is E s ∼P[V(sk+1)] = V(sk+1). Therefore, the PPO. The algorithm can appropriately allocate the power of +k+1 +proposedalgorithmdoesnotneedtouseDNNtoapproximate public and private messages to maximize the total QoE while +the value function. As a result, the estimation error resulting guaranteeingindividualQoEforeachMSP.Accordingto[95], +fromtheapproximationofthevaluefunctioncanbeprevented, the authors proposed a semantic-aware resource allocation +andthestate-actionvaluefunctioncanbecomputedaccurately. framework with a flexible duty cycle co-existence mechanism +More details about the algorithm can be found in [58]. (SARADC) algorithm that utilizes PPO to optimize resource +6) Deep Deterministic Policy Gradient: Silver et al. [166] allocation in high-speed vehicular networks. +proposed the deterministic policy gradient (DPG) algorithm We mentioned in Section II-B3 that in task-oriented +for RL problems with continuous action spaces. The deter- SemCom systems, the resource allocation is closely tied +ministic policy gradient is the expected gradient of the to the task-related importance of the semantic information. +action-valued function, which integrates over the state space Thistaskdependencenecessitatesadaptiveresourceallocation +andcanbeestimatedmoreefficientlythanthestochasticpolicy schemes that align with the utility of semantic content, +gradient. Lilicrap et al. [167] proposed the deep deterministic ensuring the transmission of task-related and semantically +policy gradient (DDPG) algorithm in the continuous action importantfeatures,whilejointlyoptimizingbandwidth,power, +space by extending DQN and DPG. DQN can only handle and computing resources for overall system performance. +discrete and low-dimensional action spaces, but many cases, However, traditional PPO methods struggle to handle such +especially physical control tasks, have continuous and high- cross-layer optimization under semantic-aware constraints. To +dimensional action spaces, and DQN cannot be directly thisend,[109]and[40]madenovelimprovementstothePPO +applied to continuous domains, so DDPG adopts the AC algorithm. Reference [109] proposes a knowledge-assisted +method based on the DPG algorithm. PPO (K-PPO) algorithm, which utilizes a prior model and +Theauthorsof[112]developedajointoptimizationproblem the Hungarian algorithm to assist PPO in solving the joint +of semantic feature compression rate, transmit power, and optimization problem of importance-aware semantic feature +bandwidth for each smart device to maximize the long-term selection and channel assignment within the joint semantic- +transmission efficiency of the task. A DDPG-based wireless channel transmission (JSCT) mechanism. Meanwhile, [40] +resource allocation scheme is proposed to efficiently handle develops an attention-enhanced PPO (APPO) by introducing +the continuous action space. the attention network [27], enabling the base station to learn +7) Twin Delayed Deep Deterministic Policy Gradient: the correlation between the semantic importance distribution +Twin delayed deep deterministic policy gradient (TD3) is f i(G i)andthetaskperformancemetricMSS,thusoptimizing +proposedbyFujimotoetal.[168]basedontheimprovementof the resource block (RB) allocation and semantic information +theDDPGalgorithm.TheTD3algorithmincorporatestheidea selection strategies accordingly. +of the double Q-learning algorithm into the DDPG algorithm. 9) Soft Actor-Critic: The soft actor-critic (SAC) algo- +Adetaileddescriptioncanreferto[161].FromACandDDPG rithm[170]isamodel-freeDRLalgorithmbasedonmaximum +to TD3, with the evolution of these RL algorithms, there entropy, introducing the concept of maximum entropy on +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2991 +TABLEX +results are difficult to interpret, which will affect the credi- +DIFFERENCEINCOMBINATIONOFDRLALGORITHMS +bility and acceptability of the results. c) DRL algorithms are +sensitivetotheselectionofhyperparametersandtrainingdata, +and the instability is higher. Table XI reviews the literature +using DRL-based centralized optimization algorithms. +C. Heuristic Algorithms +A heuristic algorithm is an algorithm based on an intuitive +or empirical construction that usually performs well with +limited computational resources and is suitable for scenarios +with low performance requirements to fulfill engineering +needs. They can provide effective approximations, but are not +guaranteed to find the global-optimal solution. +As we mentioned earlier, semantic similarity does not +have a closed-form expression. This can be regarded as a +closed-box optimization problem, which is difficult to solve +with traditional optimization algorithms. Heuristic algorithms +provide a feasible way to solve the closed-box problem. +top of maximizing future cumulative rewards to enhance Reference [88] proposed a variant of the Whale Optimization +the robustness and exploration ability of agents. In refer- Algorithm (WOA) [171] that introduces a penalty strategy: +ence [53], a dynamic intelligent resource allocation scheme the Whale Optimization Algorithm with a Penalty Strategy +was designed. It is based on SAC and D-SAC to realize (WOARA) to solve the optimal resource allocation problem. +real-time decision-making based on perceptual semantic tasks More details about WOA and WOARA can be seen in [88]. +and channel features. Among them, D-SAC is to extend SAC Theauthorsof[53]usetheparticleswarmoptimization(PSO) +to discrete space to solve the discrete variable allocation algorithmtooptimizethecompressionratioandtheallocation +problem. The Four-Soft Actor Critical (4-SAC) algorithm is of power and bandwidth for each user jointly. In [77], the +proposed in [83]. It comprises four SAC intelligent agents, PSO algorithm is developed to determine the computation +which collectively optimize the trajectory of a UAV, number resource allocation in each step of the matching game. There +of semantic symbols, and power allocation to strike a balance are also a few other works that incorporate heuristics into +between data transmission efficiency and energy efficiency, the overall program design, such as [78], which also uses a +and QL was used to facilitate learning for the optimal policy. preference list-based heuristic algorithm for problem solving. +In fact, the challenge of highly coupled and non-convex Furthermore,[66],[70],[118],[131]haveincorporatedsimple +optimization variables is particularly critical in SemCom. heuristics such as greedy algorithms into their overall pro- +Unlikeconventionalsystems,whereoptimizationvariablescan gram design. Table IX also includes papers that use heuristic +often be easily decoupled or approximated linearly. In the algorithms. +resource allocation problem of SemCom, the composition of Summary: Heuristic algorithms have some advantages in +optimization variables is very complex and hard to decouple, terms of cost and convergence speed, but their performance +which may be both in the case of discrete action space: is relatively poor, are prone to fall into local optimal, and +semantic symbol number selection, subchannel allocation, are sensitive to parameters. Therefore, they are applicable for +communication mode selection, and some discretized vari- scenarios that only have requirements on low latency and do +ables, etc., and in the case of continuous action space: power not have high demands on other performance metrics. +allocation, bandwidth allocation, semantic compression rate, In this section, centralized resource allocation optimization +etc.Totacklethiscomplexity,recentstudieschosetocombine algorithms from different literature in SemCom are reviewed. +two or more of these methods to solve the problem [47], These algorithms are categorized into several types, includ- +[55],[61],[71],[90],[94],combiningthevaluefunction-based ing those based on mathematical optimization (Lyapunov +method and the policy gradient-based methods to form a two- optimization, AO algorithm, SCA, etc.), DRL (value-based +layer DRL framework, which is also succinctly summarized and policy-based), and heuristic methods. While previous +in Table X. sections have systematically categorized performance metrics +Summary: Centralized optimization algorithms based on and optimization strategies, it is also crucial to understand +DRL are applicable to highly dynamic scenarios. They have how these elements interact across various network scenarios. +the following advantages: a) They can deal with high- Whileexistingworksproposevariousoptimizationtechniques +dimensional, nonlinear state and action spaces, making them tailored to SemCom scenarios, there remains a lack of in- +suitable for complex decision problems. b) It can adap- depth discussion on how these methods specifically address +tivelylearntheoptimalpolicywithoutexcessivemathematical the unique challenges Table XII provides a challenge-centric +derivationandcomputation.However,italsohasthefollowing synthesis of representative works, their applied optimization +disadvantages: a) High complexity of training. b) Closed-box techniques, and directions for future hybrid or enhanced +process: the learning process is unobservable, and the output methods. +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2992 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +TABLEXI +CENTRALIZEDOPTIMIZATIONALGORITHMSBASEDONDRL +V. DISTRIBUTEDRESOURCEALLOCATIONALGORITHMS +Nowadays, the network structure of wireless commu- +nications is increasingly oriented toward a multilevel +heterogeneous network structure, and efficiently managing +resource allocation in such a complex environment requires a +fundamental shift from traditional centralized mechanisms to +self-organizing and self-optimizing approaches [172]. In this +context,moreandmoredistributedmethodshavebeenutilized +to meet the increasingly complex situation. This section will Fig. 11. The taxonomy of distributed resource allocation algorithms in +SemCom. +provide an illustration of the distributed optimization algo- +rithmsusedintheliteratureonresourceallocationinSemCom, A. Multi-Agent Deep Reinforcement Learning +among them matching theory and auctions originating from +Multi-agent reinforcement learning (MARL) is the appli- +thefieldofeconomics,andaportionofreinforcementlearning +cation of reinforcement learning ideas and algorithms to +with multi-agents. Fig. 11 shows the taxonomy of centralized +multi-intelligent systems, extending MARL to deep reinforce- +resource allocation algorithms in SemCom. +ment learning is multi-agent deep reinforcement learning +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2993 +TABLEXII +MAPPINGOFSEMCOM-SPECIFICCHALLENGESTOOPTIMIZATIONSTRATEGIESANDPOTENTIALEXTENSIONS +(MADRL).In[117],directDQNisgeneralizedtomulti-agent leveraging a CTDE approach. During training, agents share +DQN,andUAVsinthecoverageareaofdifferentMECservers observations and actions to learn coordinated strategies, while +are considered agents in the DQN algorithm. However, other during execution, each agent acts independently based on its +literature utilized more refined and mature multi-intelligent own policy. Reference [123], the modified MADDPG method +body deep reinforcement learning methods, as follows: is designed to optimize both global system performance +and individual agent behavior in a dynamic semantic +1) Multi-Agent PPO/DDPG Algorithm: The multi-agent +communicationenvironment.Thesimulationresultsshowthat +PPO (MAPPO) algorithm [173] is a variant of the PPO +the proposed algorithm performs better than centralized DRL +algorithm applied to multi-agent tasks, the critic can observe +methods like DDPG and TD3. +the global state, including information about other agents and +the environment. The basic idea of the MAPPO algorithm 2) MADRL Based on Value Decomposition: MADRL +is centralized training and decentralized execution (CTDE). based on Value Decomposition (VD) is one of the many +The optimization problem of joint computational resources MADRLalgorithms.Itutilizessomeconstraintstodecompose +and bandwidth allocation is established in [126] with the the joint action-value function of a multi-agent system into a +objective of maximizing semantic accuracy. The problem is specific combination of individual action-value functions and +then transformed into a DRL framework, and MAPPO is is able to effectively solve problems such as environmental +utilized to solve the problem. non-stability and exponential explosion of the action space +MADDPG (Multi-Agent Deep Deterministic Policy in multi-agent systems, ensuring the convergence of the +Gradient)isspecificallydesignedformulti-agentsystems,also algorithm. +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2994 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +In [51], a VD-based DQN is used to allow users and BSs theBERT-basedmodel.Ingeneral,whentheBLEUscoreand +to work together to find a team RB allocation and partial similarity score are higher, the device has a greater incentive +semantic information transmission scheme to optimize the to pay a higher price for energy. +similarity of all users. The work of [42] proposed a VD- Summary: The advantages of the economic approaches +based entropy-maximized MARL (VD-ERL) algorithm. The is: effective in highly dynamic and complex heterogeneous +algorithm enables each server to coordinate its work with networks, practical in real-world scenarios. However, there +other servers in the training phase, perform RB allocation are some disadvantages, which include: a) the global optimal +in a distributed manner, and approximate the global-optimal solution may not be obtained; b) in the auction, the need +performance with fewer training iterations. for an additional third-party trusted organization for auction +Summary: The advantage of MADRL is the ability to management may incur additional costs. +solvecomplexmulti-intelligentcollaborationproblems,which In this section, we explore distributed resource allocation +is in line with the trend of increasingly complex real-world algorithms in SemCom, focusing on multi-agent deep rein- +networkchanges.Thedisadvantagesarethecomplexityofthe forcement learning (MADRL), matching theory and auction +training process and the difficulty of balancing collaboration methods. MADRL, including the multi-agent PPO and value +and competition. decomposition-based algorithms, is discussed for its ability +to handle complex coordination tasks. Economic methods, +such as matching theory and auction theory, are highlighted +B. Economic Methods +for their efficiency in decentralized resource allocation in +There are two main economic methods used in distributed dynamicenvironments.Table XIIIreviewstheliteratureusing +optimization algorithms in resource allocation of SemCom: distributed resource allocation optimization algorithms. +matchingtheoryandauction.Theyaresubfieldsofeconomics +and are promising concepts in distributed resource manage- +VI. OPENCHALLENGESANDFUTURERESEARCH +ment and allocation. +DIRECTIONS +1) Matching Theory: As a powerful tool for studying +Despite the significant achievements in resource allocation +the dynamics and mutually beneficial relationships formed +research in SemCom, many key issues remain unexplored. +between different types of agents, the matching theory +This section discusses several open research challenges and +is particularly well suited to develop practical and high- +future research directions. +performance, low-complexity, decentralized solutions in these +complex networks. In particular, it can effectively cope with +the high dynamics of the network, the selfish, competitive, A. Resource Allocation Under Other SemCom Network +and distributed nature of the network elements, the limited Architectures +wireless resources, and the QoS constraints of the different The diversity of SemCom network architectures reflects +elements [174]. its adaptability to a wide range of application scenarios, +The authors of [66] used many-to-many matching to from dynamic environments to multimodal communications. +solve the subproblem of the association between RIS users. However, without tailored resource allocation strategies, these +However,theauthorsof[22]utilizedmatchingtheorytosolve architectures cannot achieve their full potential. Developing +the subproblems of channel association and power allocation. solutions for specific frameworks is critical to maximize +In order to cope with the tight coupling between users in performance under real-world constraints. This progress will +multi-cell user and bimodal user pairs, a matching game pair expand the scope of SemCom’s applications, driving innova- +is constructed for modeling, and a low-complexity matching tions in areas such as smart homes, autonomous vehicles, and +algorithm is proposed to obtain stable matching in this part. immersive metaverse applications. +The authors of [77] establish a many-to-one matching game 1) NetworkArchitectureCombinedWithNGMA: Resource +to determine the joint communication mode and the channel allocation problem of multi-user SemCom is particularly +selection problem, in which the users and channels act as the critical in scenarios with dense user environments or limited +game players. The computational resource was allocated by communicationresources,mostofthepreviousmultipleaccess +the PSO algorithm in each step of the matching game. methods used FDMA, OFDMA, or TDMA. However, as +2) Auction: As a subfield of economics and business communication technology continues to develop, researchers +management, auction theory provides an interdisciplinary have begun to investigate the application of the combination +technique for the allocation of wireless resources (e.g., of NGMA and SemCom in resource allocation. Incorporating +subchannels, time slots, and transmit power levels) in wire- NGMA into SemCom architectures could lead to transforma- +less systems. Auction methods are widely used in areas tive advances in scenarios requiring high connectivity, such +such as cognitive radio, cellular networks, and wireless grid as smart cities, industrial IoT, and Metaverse. Currently, some +networks [175]. In [85], the bids of IoT devices (bidders) scholars have carried out research in this field; see Section II. +for energy and power transmitters (auctioneers) are used to A 2) of this article. +determine the winner and payment by competing for the 2) Network Architecture in a Dynamic Environment: +energy of the hybrid access point (H-AP) through an optimal Dynamicenvironments,suchasvehicularnetworksordisaster +auctionbasedonDL[176].TheIoTdeviceswillbidforenergy emergency communications, are highly unpredictable due to +based on sentence similarity and BLEU score derived from factors such as user mobility, interference, and time-varying +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2995 +TABLEXIII +DISTRIBUTEDOPTIMIZATIONALGORITHMSBASEDONMADRLANDECONOMICMETHODS +channel conditions. Designing resource allocation strategies standpoint,asefficientresourceallocationisessentialtoensure +that account for these dynamic characteristics is critical seamless, high-quality interactions across different types of +to ensuring robust and reliable SemCom performance. The services. At this time, the resource allocation problem for +ability to adapt to dynamic environments directly affects multimodal SemCom networks will also become a major +the network’s ability to deliver semantically accurate and challenge. +timelycommunication.Moreover,thisadaptabilityisessential +for applications like real-time AR/VR, autonomous systems, B. Establishment of SemCom Related Theory +and telemedicine, where latency and semantic accuracy are +Carnap and Bar-Hillel first proposed Classic Semantic +paramount. +Information Theory (CSIT) in 1952, based on logical proba- +3) Network Architecture of Speech SemCom: The rise of bility[177].Inspiredbythispioneeringwork,sometheoretical +speech-basedinterfacesinconsumerelectronics,smarthomes, research has been carried out in the past two decades, such +and healthcare applications highlights the need for optimized as[2]and[178],butitisnotsufficient,especiallyinSemCom +resourceallocationinspeechSemCom.Unliketextandimage based on the DL framework. This gap in foundational theory +modalities, speech signals have unique characteristics, such presents a critical opportunity for advancing SemCom, as +as real-time requirements, continuous data streams, and high developing a solid theoretical framework will enable more +sensitivity to latency and noise. Addressing these challenges effective, robust communication systems in dynamic and +inresourceallocationwillbecrucialforimprovingthequality evolving environments. +and efficiency of speech communication systems. Improved 1) Building a Universal Semantic Information Theory +resource allocation for speech SemCom could enhance appli- Framework: Compared to traditional information theory, +cations such as real-time voice recognition, smart home which has been studied for many years, the development +automation, and voice-assisted healthcare, ensuring that these of semantic information theory is relatively weak. Mainly +systems operate smoothly with minimal delay. reflected in three aspects: a) So far, there has been no +4) NetworkArchitectureofMulti-ModalSemCom: Mostof unified theoretical method for how to represent and measure +the existing SemCom network models are developed around semantics. b) SemCom lacks a comprehensive mathematical +a single modality. However, scenarios such as Metaverse basis. c) It is difficult to extend the theory of traditional +require a multi-modal service model that includes multiple information theory to semantic information theory. +types of instant interactions, such as audio, image, video, and 2) BuildingMoreAdvancedSemanticPerformanceMetrics: +tactileservices.Thisrequiresamulti-modalSemComnetwork The diversity of different scenarios and tasks in which +to solve. As a result, resource allocation for multi-modal SemCom systems are deployed means that a single static +SemCom networks becomes a critical challenge. Traditional performance metric will not suffice. Moreover, shifting +single-modal resource allocation techniques, optimized for towards a user-centric SemCom paradigm is another critical +simple scenarios, are inadequate when it comes to managing challenge.Traditionalmetrics,suchasbiterrorrates,typically +the dynamic and diverse needs of multi-modal data streams. focus on technical performance, but do not capture the real- +This problem is even more important from a user-centric world effectiveness of SemCom systems from the user’s +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +2996 IEEECOMMUNICATIONSSURVEYS&TUTORIALS,VOLUME28,2026 +TABLEXIV +CHALLENGESANDFUTUREDIRECTIONS +perspective. Establishing such advanced metrics will enable a are particularly useful for modeling complex, structured data, +moreaccurateevaluationofasystem’sabilitytoprovidevalue suchasnetworktopologiesorrelationshipsbetweendevicesin +to users, beyond just raw data transmission efficiency. a distributed SemCom environment. GNNs can help optimize +communication pathways and improve resource allocation by +modeling dependencies between nodes in real time, problems +C. SemCom Resource Allocation Optimization Scheme +partially modeled as combination optimization problems, or +1) CombinationofMultipleAlgorithms: Theresourceallo- +other special scenarios. A simple outline below illustrates +cation problem can be solved by combining mathematical +how GNNs could help address channel assignment as a +optimization and the RL method to save computing resources +combinatorial problem in semantic communication systems: +andachievetheoptimalsolution.Similarly,combiningheuris- +1) Graph modeling: Represent users and interference links +tic algorithms with optimization allows fast, near-optimal +as a graph. +solutions in time-sensitive situations. The impact of these +2) Problem setup: Frame channel assignment as a combi- +combined techniques is significant: improved service quality, +natorial optimization problem. +reduced energy consumption, and the ability to meet the +3) GNN encoding: Use GNNs to learn node embeddings +demands of the growing user. +that capture interference and task demands. +2) FL-Enabled and Other DL-Enabled Techniques: +4) Solution generation: Predict channel assignments +Federated learning (FL) is a machine learning technology +directly or guide heuristics with learned scores. +that can train resource scheduling algorithms on multiple +5) Training feedback: Optimize GNN using corresponding +distributed edge devices or servers that do not exchange local +metrics like S-SE. +data samples [179]. FL allows edge devices to collaboratively +Diffusion models, with their generative capabilities, may +learn resource scheduling policies without sharing raw +support joint optimization of semantic compression and +semantic data. This decentralized training paradigm naturally +resource usage under strict delay or accuracy constraints. +protects user privacy, making it well-suited for privacy- +Here are some open research issues about utilizing these DL- +sensitive SemCom applications such as telemedicine or +enabled techniques in resource allocation in SemCom: +autonomous driving. While this approach remains largely +• Communicationoverheadforfrequentmodelupdatescan +unexplored, the following outline presents one possible way +be significant in FL. +to incorporate federated learning into semantic resource +• Lightweight FL protocols are needed for resource- +allocation: +constrained devices. +1) Alocalmodelistrainedoneachdevicetomakeresource +• Scalability challenges for large-scale distributed systems +allocation schemes (e.g., semantic compression ratio, +and difficulty in integrating multiple objectives (e.g., +offloading decision). +latency, accuracy, energy) into GNN-based models. +2) Devices send model updates (e.g., gradients or parame- +• High computational cost of diffusion models may hinder +ters) to a central aggregator. +real-time deployment. +3) The server performs model aggregation and updates the +global model. +D. Other Challenges +4) Theglobalmodelisredistributedtodevicesforthenext +training round. There are still some other challenges that need to be +Additionally, other deep learning techniques, such as dif- considered in the resource allocation of SemCom.32 +fusion models and graph neural networks (GNNs), can also 1) Transformation of Focus: Resource allocation in +play a crucial role in resource allocation optimization. GNNs SemComisundergoingashiftfromsystem-leveloptimization +Authorized licensed use limited to: WUHAN UNIVERSITY OF TECHNOLOGY. Downloaded on February 09,2026 at 07:23:36 UTC from IEEE Xplore. Restrictions apply. + +---PAGE BREAK--- + +ZHANGetal.:RESOURCEALLOCATIONINWIRELESSSem-Com:ACOMPREHENSIVESURVEY 2997 +to a user-centric approach. Traditionally, resource allocation alsosummarizeandexplainthenetworkstructureandresource +schemes in SemCom primarily aim to maximize system- allocation types in these studies, emphasize the performance +wide performance metrics, such as throughput, latency, indicators and resource allocation optimization algorithms in +or energy efficiency. However, with the growing demand these studies, and provide detailed tables to summarize these +for personalized services and the increasing diversity of studies. We identify current research bottlenecks and chal- +user requirements, future resource allocation schemes must lenges in the allocation of SemCom resources and anticipate +prioritize individual user satisfaction. Despite its importance, further research in the future. We hope that our work can +transforming resource allocation into a user-centric paradigm provide references and insight to future researchers, as well +posessignificantchallenges.Oneofthebiggestobstaclesisthe as encourage follow-up research. +design of meaningful and measurable metrics that accurately +reflectusersatisfaction,asitrequiresaccountingforsubjective +ACKNOWLEDGMENT +factors such as preferences and context. Additionally, these +metricsneedtobedynamicandadaptabletovaryingscenarios, The authors would like to thank the anonymous reviewers +such as real-time changes in user behavior or network for their valuable comments and suggestions, which helped to +conditions. Another challenge lies in the inherent complexity improve the quality of this article. +of resource allocation to meet diverse and sometimes +conflictinguserneeds.Forexample,balancingthesatisfaction REFERENCES +of multiple users while ensuring the fairness and efficient +[1] C. E. Shannon and W. 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