Channel modeling is a critical issue when designing or evaluating the performance of reconfigurable intelligent surface (RIS)-assisted communications. Inspired by the promising potential of learning-based methods for characterizing the radio environment, we present a general approach to model the RIS end-to-end equivalent channel using the unsupervised expectation-maximization (EM) learning algorithm. We show that an EM-based approximation through a simple mixture of two Nakagami- m{m} distributions suffices to accurately approximate the equivalent channel, while allowing for the incorporation of crucial aspects into RIS’s channel modeling such as beamforming, spatial channel correlation, phase-shift errors, arbitrary fading conditions, and coexistence of direct and RIS channels. Based on the proposed analytical framework, we evaluate the outage probability under different settings of RIS’s channel features and confirm the superiority of this approach compared to recent results in the literature.

中文翻译：

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可重构智能表面无线信道建模的期望最大化学习


在设计或评估可重构智能表面（RIS）辅助通信的性能时，信道建模是一个关键问题。受到基于学习的无线电环境表征方法的巨大潜力的启发，我们提出了一种使用无监督期望最大化 (EM) 学习算法对 RIS 端到端等效信道进行建模的通用方法。我们证明，通过两个 Nakagami-m{m} 分布的简单混合进行的基于 EM 的近似足以精确地近似等效信道，同时允许将关键方面纳入 RIS 的信道建模中，例如波束成形、空间信道相关性、相位- 移位错误、任意衰落条件以及直接通道和 RIS 通道的共存。基于所提出的分析框架，我们评估了 RIS 通道特征的不同设置下的中断概率，并确认了该方法与文献中最新结果相比的优越性。