Deep-Learning-Assisted Configuration of Reconfigurable Intelligent Surfaces in Dynamic rich-scattering Environments

TL;DR

This paper proposes a deep learning and genetic algorithm-based method to optimize reconfigurable intelligent surfaces in complex, dynamic rich-scattering wireless environments, improving communication rates.

Contribution

It introduces a novel approach combining a neural network surrogate model with genetic algorithms for RIS configuration in dynamic rich-scattering scenarios, addressing a key challenge.

Findings

Neural network accurately models stochastic channel dependence on RIS configurations.

Genetic algorithm effectively finds RIS settings that enhance communication rates.

Method demonstrates significant rate improvements in simulated rich-scattering environments.

Abstract

The integration of Reconfigurable Intelligent Surfaces (RISs) into wireless environments endows channels with programmability, and is expected to play a key role in future communication standards. To date, most RIS-related efforts focus on quasi-free-space, where wireless channels are typically modeled analytically. Many realistic communication scenarios occur, however, in rich-scattering environments which, moreover, evolve dynamically. These conditions present a tremendous challenge in identifying an RIS configuration that optimizes the achievable communication rate. In this paper, we make a first step toward tackling this challenge. Based on a simulator that is faithful to the underlying wave physics, we train a deep neural network as surrogate forward model to capture the stochastic dependence of wireless channels on the RIS configuration under dynamic rich-scattering conditions. Subsequently, we use this model in combination with a genetic algorithm to identify RIS configurations optimizing the communication rate. We numerically demonstrate the ability of the proposed approach to tune RISs to improve the achievable rate in rich-scattering setups.