198 lines
7.7 KiB
Python
198 lines
7.7 KiB
Python
"""
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无线资源分配信道模型 / Channel model for OFDMA wireless resource allocation.
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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。
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所有公式遵循论文中的公式 (5)-(8)。
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This module implements the 3GPP-style path loss model and complex channel gain generation
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for a multi-user OFDMA downlink system. All formulas follow the paper's equations (5)–(8).
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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 ChannelModel:
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"""
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多用户 OFDMA 系统的频率选择性信道模型。
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Frequency-selective channel model for multi-user OFDMA systems.
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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)。
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Generates per-subcarrier complex channel gains incorporating distance-dependent
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path loss with Rayleigh fading, and computes per-subcarrier SNR values.
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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 carrier_freq, noise_psd 和 subcarrier_spacing 键）。
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Full configuration dictionary (must contain an "env" section with keys
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"carrier_freq", "noise_psd", and "subcarrier_spacing").
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"""
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def __init__(self, config: dict) -> None:
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# 初始化环境配置 / Initialize environment configurations
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self.config = config
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env = config["env"]
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# 载波频率 (GHz) / Carrier frequency in GHz
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self._carrier_freq_ghz: float = env["carrier_freq"]
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# 噪声功率谱密度 (dBm/Hz) / Noise power spectral density in dBm/Hz
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self._noise_psd_dbm: float = env["noise_psd"]
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# 子载波间隔 (Hz) / Subcarrier spacing in Hz
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self._subcarrier_spacing: float = env["subcarrier_spacing"]
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# ------------------------------------------------------------------
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# 路径损耗 / Path loss
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# ------------------------------------------------------------------
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def path_loss(self, distance: float) -> float:
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"""
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计算与距离相关的路径损耗 (dB)。
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Compute distance-dependent path loss in dB.
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使用 3GPP Urban Micro (UMi) NLOS 模型 (公式 5):
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Uses the 3GPP Urban Micro (UMi) NLOS model (Eq. 5):
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PL(d) = 36.7 * log10(d) + 22.7 + 26 * log10(fc)
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其中 d 的单位为米，fc 的单位为 GHz。
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where *d* is in metres and *fc* is in GHz.
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Parameters
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----------
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distance : float or np.ndarray
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收发机之间的距离，单位为米。
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Transmitter–receiver distance(s) in metres.
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Returns
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-------
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float or np.ndarray
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路径损耗值，单位为 dB。
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Path loss value(s) in dB.
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"""
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fc = self._carrier_freq_ghz
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# 应用 3GPP UMi NLOS 公式 / Apply 3GPP UMi NLOS formula - Eq.(5)
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return 36.7 * np.log10(distance) + 22.7 + 26.0 * np.log10(fc)
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# ------------------------------------------------------------------
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# 信道生成 / Channel generation
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# ------------------------------------------------------------------
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def generate_channel(
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self, distances: np.ndarray, num_subcarriers: int
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) -> np.ndarray:
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"""
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生成所有用户和子载波的复信道增益。
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Generate complex channel gains for all users and subcarriers.
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每个元素 h_{k,n} 服从复高斯分布 CN(0, 10^{-PL/10}) (公式 6)。
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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。
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Each element h_{k,n} is drawn from CN(0, 10^{-PL/10}) (Eq. 6), i.e.
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independent circularly-symmetric complex Gaussian with variance
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equal to the linear-scale inverse path loss.
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Parameters
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----------
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distances : array_like, shape (K,)
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每个用户距离基站的距离（米）。
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Distance of each user from the base station (metres).
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num_subcarriers : int
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OFDM 子载波数量 N。
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Number of OFDM subcarriers *N*.
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Returns
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-------
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np.ndarray, shape (K, N)
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复信道增益矩阵。
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Complex channel gain matrix.
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"""
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distances = np.asarray(distances, dtype=np.float64)
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K = len(distances)
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N = num_subcarriers
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# 每用户路径损耗 -> 线性尺度信道方差 / Per-user path loss -> linear-scale channel variance
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pl_db = self.path_loss(distances) # (K,)
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# 方差 = 10^(-PL/10) / Variance = 10^(-PL/10) - Eq.(6)
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variance = 10.0 ** (-pl_db / 10.0) # (K,)
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variance = variance.reshape(K, 1) # (K, 1) 用于广播 / for broadcasting
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# 复高斯：每个分量服从 N(0, var/2) / Complex Gaussian: each component ~ N(0, var/2)
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std = np.sqrt(variance / 2.0)
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# 生成实部和虚部 / Generate real and imaginary parts
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real_part = np.random.randn(K, N) * std
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imag_part = np.random.randn(K, N) * std
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# 返回复增益 / Return complex gains
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return real_part + 1j * imag_part
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# ------------------------------------------------------------------
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# SNR 计算 / SNR computation
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# ------------------------------------------------------------------
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def compute_snr(
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self,
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channel_gains: np.ndarray,
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power_alloc: np.ndarray,
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noise_power: float,
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) -> np.ndarray:
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"""
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计算每个用户的每子载波信噪比 (SNR)。
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Compute per-subcarrier SNR for every user.
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γ_{k,n} = p_{k,n} * |h_{k,n}|² / σ² (公式 8)
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γ_{k,n} = p_{k,n} · |h_{k,n}|² / σ² (Eq. 8)
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Parameters
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----------
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channel_gains : np.ndarray, shape (K, N)
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复信道增益矩阵。
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Complex channel gain matrix.
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power_alloc : np.ndarray, shape (K, N)
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每个用户在每个子载波上分配的功率（瓦特）。
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Power allocated by each user on each subcarrier (Watts).
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noise_power : float
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每子载波的噪声功率 σ²（瓦特）。
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Noise power σ² per subcarrier (Watts).
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Returns
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-------
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np.ndarray, shape (K, N)
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SNR 值（线性尺度）。
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SNR values (linear scale).
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"""
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# 计算 SNR = 功率 * 增益平方 / 噪声 / Compute SNR = Power * Gain^2 / Noise - Eq.(8)
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return power_alloc * (np.abs(channel_gains) ** 2) / noise_power
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# ------------------------------------------------------------------
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# 噪声功率属性 / Noise power property
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# ------------------------------------------------------------------
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@property
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def noise_power(self) -> float:
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"""
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每子载波的热噪声功率 (瓦特)。
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Thermal noise power per subcarrier (Watts).
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σ² = N₀ * Δf (公式 7)
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σ² = N₀ · Δf (Eq. 7)
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其中 N₀ 是从 dBm/Hz 转换为线性 (W/Hz) 的噪声功率谱密度：
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where N₀ is the noise PSD converted from dBm/Hz to linear (W/Hz):
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N₀_linear = 10^((N₀_dBm - 30) / 10)
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Returns
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-------
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float
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噪声功率（瓦特）。
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Noise power in Watts.
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"""
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n0_dbm = self._noise_psd_dbm
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delta_f = self._subcarrier_spacing
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# 转换为线性功率谱密度 / Convert to linear PSD - Eq.(7)
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n0_linear = 10.0 ** ((n0_dbm - 30.0) / 10.0)
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# 计算总噪声功率 / Compute total noise power
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return n0_linear * delta_f
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