2021/03/24 by José David Vega Sánchez, Luis Urquiza-Aguiar, Sánchez, José David Vega +5 · 1 citation
Engineering · #Advanced Wireless Communication Techniques #Advanced Wireless Communication Technologies #FOS: Computer and information sciences #Information Theory (cs.IT) #Millimeter-Wave Propagation and Modeling
paper · pdf · doi:10.48550/arxiv.2103.13525
openalex publication_date 2021/03/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Channel modeling is a critical issue when designing or evaluating the\nperformance of reconfigurable intelligent surface (RIS)-assisted\ncommunications. Inspired by the promising potential of learning-based methods\nfor characterizing the radio environment, we present a general approach to\nmodel the RIS end-to-end equivalent channel using the unsupervised\nexpectation-maximization (EM) learning algorithm. We show that an EM-based\napproximation through a simple mixture of two Nakagami-m distributions\nsuffices to accurately approximating the equivalent channel, while allowing for\nthe incorporation of crucial aspects into RIS's channel modeling as spatial\nchannel correlation, phase-shift errors, arbitrary fading conditions, and\ncoexistence of direct and RIS channels. Based on the proposed analytical\nframework, we evaluate the outage probability under different settings of RIS's\nchannel features and confirm the superiority of this approach compared to\nrecent results in the literature.\n