Wireless channel models are a commonly used tool for the development of wireless telecommunication systems and standards. The currently prevailing geometry-based stochastic channel models (GSCMs) were manually specified for certain environments in a manual process requiring extensive domain knowledge, on the basis of channel measurement campaigns. By taking into account the stochastic distribution of certain channel properties like Rician k-factor, path loss or delay spread, they model the distribution of channel realizations. Instead of this manual process, a generative machine learning model like a generative adversarial network (GAN) may be used to automatically learn the distribution of channel statistics. Subsequently, the GAN's generator may be viewed as a channel model that can replace conventional stochastic or raytracer-based models. We propose a GAN architecture for a massive MIMO channel model, and train it on measurement data produced by a distributed massive MIMO channel sounder.

翻译：无线信道模型是无线通信系统及标准开发中常用的工具。当前主流的基于几何的随机信道模型（GSCMs）是针对特定环境，通过人工流程并依赖大量领域知识，基于信道测量活动手动指定的。这些模型通过考虑某些信道特性（如莱斯k因子、路径损耗或时延扩展）的随机分布，对信道实现分布进行建模。不同于这种人工流程，可借助生成对抗网络（GAN）等生成式机器学习模型，自动学习信道统计特性的分布。进而，GAN的生成器可视为替代传统随机模型或射线追踪模型的信道模型。我们提出一种适用于大规模MIMO信道模型的GAN架构，并利用分布式大规模MIMO信道探测仪生成的测量数据对其进行训练。