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-$m$ distributions suffices to accurately approximating the equivalent channel, while allowing for the incorporation of crucial aspects into RIS's channel modeling as 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.

翻译：在设计或评价可重新配置的智能表面辅助通信的性能时,频道建模是一个关键问题。在基于学习的无线电环境特征化方法的极有潜力的启发下,我们提出了一个通用方法,利用无人监督的预期-最大化学习算法,模拟RIS端对端等频道。我们表明,通过两种中上-百万美元分布法的简单组合,基于EM的近似就足以准确接近等同频道,同时允许将关键方面纳入RIS的频道建模,如空间信道的相互关系、阶段变错误、任意消减条件以及直接和RIS频道共存。我们根据拟议的分析框架,评估RIS频道特征不同环境下的外差概率,并证实这一方法与文献中最近的结果相比具有优势。