Deep Learning-Based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations

TL;DR

This paper introduces a deep learning method that leverages correlations in previously sampled channels to improve RIS configuration, reducing the need for extensive channel estimation and beam training.

Contribution

It proposes a novel deep learning approach that exploits channel correlations for more reliable RIS interaction estimation, unlike prior methods.

Findings

Performance improvements demonstrated in simulations

Reduces beam training overhead

Enhances RIS configuration accuracy

Abstract

The reconfigurable intelligent surface (RIS) technology has attracted interest due to its promising coverage and spectral efficiency features. However, some challenges need to be addressed to realize this technology in practice. One of the main challenges is the configuration of reflecting coefficients without the need for beam training overhead or massive channel estimation. Earlier works used estimated channel information with deep learning algorithms to design RIS reflection matrices. Although these works can reduce the beam training overhead, still they overlook existing correlations in the previously sampled channels. In this paper, different from existing works, we propose to exploit the correlation in the previously sampled channels to estimate RIS interaction more reliably. We use a deep multi-layer perceptron for this purpose. Simulation results reveal performance improvements achieved by the proposed algorithm.