Analysis and Optimization of Reconfigurable Intelligent Surfaces Based on $S$-Parameters Multiport Network Theory

TL;DR

This paper models reconfigurable intelligent surfaces using multiport network theory, compares $Z$- and $S$-parameters, and develops an optimized algorithm based on $S$-parameters that outperforms existing methods.

Contribution

It introduces a novel $S$-parameter based optimization algorithm for RIS configuration that improves convergence speed and performance.

Findings

The $S$-parameter model provides better optimization performance.

The proposed algorithm converges faster due to larger $S$-parameter variations.

Comparison shows $S$-parameters are more effective than $Z$-parameters for RIS optimization.

Abstract

In this paper, we consider a reconfigurable intelligent surface (RIS) and model it by using multiport network theory. We first compare the representation of RIS by using $Z$-parameters and $S$-parameters, by proving their equivalence and discussing their distinct features. Then, we develop an algorithm for optimizing the RIS configuration in the presence of electromagnetic mutual coupling. We show that the proposed algorithm based on optimizing the $S$-parameters results in better performance than existing algorithms based on optimizing the $Z$-parameters. This is attributed to the fact that small perturbations of the step size of the proposed algorithm result in larger variations of the $S$-parameters, hence increasing the convergence speed of the algorithm.