Statistical prior channel knowledge, such as the wide-sense-stationary-uncorrelated-scattering (WSSUS) property, and additional side information both can be used to enhance physical layer applications in wireless communication. Generally, the wireless channel's strongly fluctuating path phases and WSSUS property characterize the channel by a zero mean and Toeplitz-structured covariance matrices in different domains. In this work, we derive a framework to comprehensively categorize side information based on whether it preserves or abandons these statistical features conditioned on the given side information. To accomplish this, we combine insights from a generic channel model with the representation of wireless channels as probabilistic graphs. Additionally, we exemplify several applications, ranging from channel modeling to estimation and clustering, which demonstrate how the proposed framework can practically enhance physical layer methods utilizing machine learning (ML).

翻译：统计先验信道知识，如广义平稳非相关散射（WSSUS）特性，以及额外的侧信息，均可用于增强无线通信中的物理层应用。通常，无线信道强烈波动的路径相位及其WSSUS特性，通过不同域中的零均值和Toeplitz结构协方差矩阵来表征信道。在本工作中，我们推导出一个框架，根据侧信息在给定条件下是保留还是舍弃这些统计特征，来全面分类侧信息。为实现这一目标，我们将通用信道模型的见解与无线信道的概率图表示相结合。此外，我们列举了从信道建模到估计与聚类的若干应用实例，以展示所提框架如何实际增强利用机器学习（ML）的物理层方法。