基于边信息条件下的无线信道统计特性刻画
A Statistical Characterization of Wireless Channels Conditioned on Side Information
AI总结：
本文提出一个基于概率图的框架，根据边信息是否保留WSSUS等统计特征对其进行分类，并通过信道建模、估计与聚类等应用展示其对机器学习物理层方法的增强作用。
摘要：
统计先验信道知识，例如广义平稳非相关散射（WSSUS）特性，以及额外的边信息，均可用于增强无线通信中的物理层应用。通常，无线信道剧烈波动的路径相位和WSSUS特性使得信道在不同域中由零均值及Toeplitz结构的协方差矩阵来表征。本文推导了一个框架，用于根据在给定边信息条件下这些统计特征是被保留还是被舍弃，对边信息进行全面分类。为此，我们将通用信道模型的见解与无线信道作为概率图的表示方法相结合。此外，我们举例说明了从信道建模到估计和聚类等多个应用，展示了所提框架如何在实际中增强利用机器学习（ML）的物理层方法。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
