Experimental Reduced-Rank Mutual Coupling Representation and Estimation for Large RIS

TL;DR

This paper introduces a reduced-rank mutual coupling model for large RISs, enabling practical estimation and optimization by balancing complexity and accuracy, validated through experiments in various radio environments.

Contribution

A novel flexible reduced-rank mutual coupling representation for large RISs, with experimental validation across different radio environments.

Findings

Reduced-rank model effectively estimates mutual coupling in large RISs.

Environmental factors significantly influence the accuracy of rank reduction.

Mutual coupling awareness impacts optimization performance depending on the environment.

Abstract

Physics-consistent optimization of reconfigurable intelligent surfaces (RISs) is thwarted in practice by the difficulty of experimentally estimating the mutual coupling (MC) between RIS elements. For large RISs, experimental MC estimation is fundamentally challenging because of the quadratic scaling of the number of unknowns with the number of RIS elements. In this Letter, we present a generic and flexible reduced-rank MC representation that allows wireless practitioners to choose a trade-off between model complexity and accuracy. We experimentally validate the direct reduced-rank MC estimation for a 100-element RIS in three radio environments (rich scattering, attenuated scattering, free space). We observe a strong environmental dependence of the influence of rank reduction on accuracy. Model-based performance evaluations highlight that the importance of MC awareness in optimization depends strongly on the radio environment and the performance indicator.