A self-adaptive RIS that estimates and shapes fading rich-scattering wireless channels

TL;DR

This paper introduces a self-adaptive reconfigurable intelligent surface (RIS) framework that estimates dynamic environmental factors and optimizes channel shaping in complex fading wireless environments, advancing RIS applications.

Contribution

It proposes a novel method for RIS to estimate perturbers and adapt configurations using a learned model, enabling enhanced channel control in rich-scattering environments.

Findings

Demonstrates RIS adaptation in a physics-based model with fading

Shows improved signal strength through optimized RIS configurations

Bridges RIS localization, sensing, and channel shaping in complex environments

Abstract

We present a framework for operating a self-adaptive RIS inside a fading rich-scattering wireless environment. We model the rich-scattering wireless channel as being double-parametrized by (i) the RIS, and (ii) dynamic perturbers (moving objects, etc.). Within each coherence time, first, the self-adaptive RIS estimates the status of the dynamic perturbers (e.g., the perturbers' orientations and locations) based on measurements with an auxiliary wireless channel. Then, second, using a learned surrogate forward model of the mapping from RIS configuration and perturber status to wireless channel, an optimized RIS configuration to achieve a desired functionality is obtained. We demonstrate our technique using a physics-based end-to-end model of RIS-parametrized communication with adjustable fading (PhysFad) for the example objective of maximizing the received signal strength indicator. Our results present a route toward convergence of RIS-empowered localization and sensing with RIS-empowered channel shaping beyond the simple case of operation in free space without fading.