In the realm of wireless communication, stochastic modeling of channels is instrumental for the assessment and design of operational systems. Deep learning neural networks (DLNN), including generative adversarial networks (GANs), are being used to approximate wireless Orthogonal frequency-division multiplexing (OFDM) channels with fading and noise, using real measurement data. These models primarily focus on channel output (y) distribution given input x: p(y|x), limiting their application scope. DLNN channel models have been tested predominantly on simple simulated channels. In this paper, we build both GANs and feedforward neural networks (FNN) to approximate a more general channel model, which is represented by a conditional probability density function (PDF) of receiving signal or power of node receiving power Prx: f_p_rx|d(()), where is communication distance. The stochastic models are trained and tested for the impact of fading channels on transmissions of OFDM QAM modulated signal and transmissions of general signal regardless of modulations. New metrics are proposed for evaluation of modeling accuracy and comparisons of the GAN-based model with the FNN-based model. Extensive experiments on Nakagami fading channel show accuracy and the effectiveness of the approaches.

翻译：在无线通信领域，信道的随机建模对于运营系统的评估与设计至关重要。深度学习神经网络（DLNN），包括生成对抗网络（GAN），正被用于利用实测数据逼近存在衰落与噪声的无线正交频分复用（OFDM）信道。这些模型主要关注给定输入x条件下信道输出y的分布p(y|x)，这一局限性制约了其应用范围。目前，DLNN信道模型主要针对简单仿真信道进行测试。本文构建了GAN与前馈神经网络（FNN）来逼近更通用的信道模型，该模型由接收信号或节点接收功率Prx的条件概率密度函数（PDF）f_{Prx|d}(\cdot)表征，其中d为通信距离。我们训练并测试了随机模型在衰落信道中对OFDM QAM调制信号以及无调制约束通用信号传输的影响。提出新的指标用于评估建模精度，并比较基于GAN与基于FNN的模型性能。在Nakagami衰落信道上开展的大量实验验证了所提方法的准确性与有效性。