Impact of Channel Models on Performance Characterization of RIS-Assisted Wireless Systems

TL;DR

This paper reviews five progressively accurate channel models for RIS-assisted wireless systems, analyzing their impact on performance estimates and introducing a new scalable configuration algorithm for large RISs.

Contribution

It provides a comprehensive comparison of channel models and proposes a novel scalable algorithm for configuring very large RISs.

Findings

Performance estimates vary significantly across models.

More accurate models lead to better system design insights.

The new algorithm improves scalability for large RIS configurations.

Abstract

The performance characterization of communication systems assisted by large reconfigurable intelligent surfaces (RISs) significantly depends on the adopted models for the underlying channels. Under unrealistic channel models, the system performance may be over- or under-estimated which yields inaccurate conclusions for the system design. In this paper, we review five channel models that are chosen to progressively improve the modeling accuracy for large RISs. For each channel model, we highlight the underlying assumptions, its advantages, and its limitations. We compare the system performance under the aforementioned channel models using RIS configuration algorithms from the literature and a new scalable algorithm proposed in this paper specifically for the configuration of extremely large RISs.