Statistical Multiport-Network Modeling and Efficient Discrete Optimization of RIS

TL;DR

This paper develops a statistical multiport-network model for RIS channels considering mutual coupling and 1-bit elements, and evaluates optimization strategies for efficient RIS configuration.

Contribution

It introduces a physics-consistent statistical generator for RIS channel parameters including mutual coupling, and benchmarks various optimization algorithms under realistic conditions.

Findings

Coordinate descent with random initialization performs best when mutual coupling is significant.

The proposed model accounts for reciprocity, passivity, and backscattering in RIS channels.

Insights guide practical optimization of RIS prototypes and reconfigurable wave systems.

Abstract

This Letter addresses the physics-consistent optimization of reconfigurable intelligent surfaces (RISs) with mutual coupling (MC) and 1-bit-programmable RIS elements. This combination of constraints is typical of current prototypes but unexplored in theoretical work. First, we present a simple statistical generator for multiport-network-theory (MNT) parameters of rich-scattering, RIS-parametrized channels. We account for reciprocity, passivity, and coherent backscattering; then, we add a simple hyper-parameter to control the MC strength. Second, we benchmark model-agnostic (dictionary search, coordinate descent, genetic algorithm) and model-based (temperature-annealed back-propagation) strategies under varying MC, with and without intelligent initialization. Except when MC is negligible, coordinate descent with random initialization offers the best trade-off in performance, runtime, and memory. Our insights can guide wireless practitioners who optimize RIS prototypes and other reconfigurable wave systems.