157 lines
5.8 KiB
Python
157 lines
5.8 KiB
Python
"""
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语义通信模块 / Semantic communication module for Co-MADDPG.
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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。
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Implements semantic similarity (SSim) computation based on empirical DeepSC curves,
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and QoE calculation for semantic communication users.
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作者/Author: Sisyphus-Junior
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日期/Date: 2026-02-28
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论文引用/Paper Reference: Co-MADDPG based Resource Allocation for Semantic Communication
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依赖/Dependencies: numpy
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"""
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import numpy as np
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class SemanticModule:
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"""
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语义通信质量模块。
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Semantic communication quality module.
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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)。
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Computes semantic similarity index (SSim) from average SNR and compression
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ratio, using empirical fitting curves inspired by DeepSC literature.
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Parameters
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----------
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config : dict
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完整的配置字典（必须包含 "env" 部分，且具有 rho_max, rho_min, w1, w2 键）。
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Full configuration dictionary (must contain an "env" section
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with keys "rho_max", "rho_min", "w1", "w2").
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"""
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def __init__(self, config: dict) -> None:
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# 初始化语义参数 / Initialize semantic parameters
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self.config = config
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env = config["env"]
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# 最大压缩率 / Maximum compression ratio ρ_max
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self.rho_max = env.get("rho_max", 1.0)
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# 最小压缩率 / Minimum compression ratio ρ_min
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self.rho_min = env.get("rho_min", 0.05)
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# QoE 权重 1 / QoE Weight w1 (SSim weight)
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self.w1 = env.get("w1", 0.7)
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# QoE 权重 2 / QoE Weight w2 (Resource efficiency weight)
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self.w2 = env.get("w2", 0.3)
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@staticmethod
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def _a(rho: float) -> float:
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"""经验曲线参数 a(ρ) = 0.8 / (ρ + 0.1)。 / Empirical curve parameter a(ρ) = 0.8 / (ρ + 0.1)."""
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return 0.8 / (rho + 0.1)
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@staticmethod
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def _b(rho: float) -> float:
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"""经验曲线参数 b(ρ) = 0.6 + 0.2 * ρ。 / Empirical curve parameter b(ρ) = 0.6 + 0.2 * ρ."""
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return 0.6 + 0.2 * rho
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def compute_ssim(self, avg_snr, rho: float):
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"""
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计算语义相似度指数 (SSim)。
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Compute semantic similarity index (SSim).
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φ(γ̄, ρ) = 1 - exp(-a(ρ) * γ̄^{b(ρ)}) (公式参考 SSim 章节)
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φ(γ̄, ρ) = 1 - exp(-a(ρ) * γ̄^{b(ρ)}) (Refer to SSim section in paper)
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Parameters
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----------
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avg_snr : float or np.ndarray
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线性尺度的平均 SNR（非 dB）。
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Average SNR in linear scale (not dB).
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rho : float
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压缩率 ρ ∈ [ρ_min, ρ_max]。
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Compression ratio ρ ∈ [ρ_min, ρ_max].
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Returns
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-------
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float or np.ndarray
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[0, 1] 范围内的语义相似度。
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Semantic similarity in [0, 1].
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"""
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# 防止 SNR 过小导致数值错误 / Avoid numerical errors with small SNR
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avg_snr = np.maximum(avg_snr, 1e-10)
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# 获取经验参数 a 和 b / Get empirical parameters a and b
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a = self._a(rho)
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b = self._b(rho)
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# 计算 SSim 公式 / Compute SSim formula
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return 1.0 - np.exp(-a * np.power(avg_snr, b))
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def compute_avg_snr(self, snr_per_subcarrier: np.ndarray,
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allocation_mask: np.ndarray) -> float:
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"""
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计算已分配子载波上的平均 SNR。
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Compute average SNR over allocated subcarriers.
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Parameters
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----------
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snr_per_subcarrier : np.ndarray
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所有子载波的 SNR 值（线性尺度）。
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SNR values for all subcarriers (linear scale).
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allocation_mask : np.ndarray
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指示已分配子载波的二进制掩码。
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Binary mask indicating allocated subcarriers.
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Returns
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-------
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float
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已分配子载波的平均 SNR；若未分配则返回 0。
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Mean SNR over allocated subcarriers; 0 if none allocated.
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"""
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# 提取已分配子载波的 SNR / Extract SNR for allocated subcarriers
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allocated = snr_per_subcarrier[allocation_mask > 0]
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# 如果没有子载波被分配 / If no subcarriers are allocated
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if len(allocated) == 0:
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return 0.0
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# 返回平均值 / Return mean value
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return float(np.mean(allocated))
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def compute_semantic_qoe(self, ssim: float, rho: float,
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w1: float = None, w2: float = None,
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rho_max: float = None) -> float:
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"""
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计算语义通信用户的 QoE。
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Compute QoE for a semantic communication user.
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QoE_s = w1 * SSim + w2 * (1 - ρ / ρ_max) (公式参考 QoE_s)
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QoE_s = w1 * SSim + w2 * (1 - ρ / ρ_max) (Refer to QoE_s formula)
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Parameters
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----------
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ssim : float
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[0, 1] 范围内的语义相似度指数。
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Semantic similarity index in [0, 1].
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rho : float
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使用的压缩率。
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Compression ratio used.
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w1, w2 : float, optional
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权重（默认为配置中的实例值）。
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Weights (defaults to instance values from config).
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rho_max : float, optional
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最大压缩率（默认为配置中的值）。
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Maximum compression ratio (default from config).
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Returns
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-------
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float
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[0, 1] 范围内的 QoE 值。
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QoE value in [0, 1].
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"""
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# 使用默认值或输入值 / Use default or input values
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if w1 is None:
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w1 = self.w1
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if w2 is None:
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w2 = self.w2
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if rho_max is None:
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rho_max = self.rho_max
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# 计算语义 QoE / Calculate semantic QoE
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return float(w1 * ssim + w2 * (1.0 - rho / rho_max))
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