Expectation-Maximization Learning for Wireless Channel Modeling of Reconfigurable Intelligent Surfaces

TL;DR

This paper introduces an EM-based method for modeling RIS-assisted wireless channels, accurately capturing complex channel features and outperforming existing models in outage probability evaluations.

Contribution

It proposes a novel unsupervised EM algorithm using a mixture of two Nakagami-m distributions for detailed RIS channel modeling, including correlation and phase errors.

Findings

Accurately models RIS channels with spatial correlation and phase errors.

Outperforms recent models in outage probability assessments.

Provides a flexible analytical framework for RIS channel analysis.

Abstract

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.