Ray Tracing-Enabled Digital Twin for RIS Phase Optimization: Implementation and Experimental Validation

TL;DR

This paper introduces a digital twin framework using ray tracing for optimizing RIS phase shifts, reducing signaling overhead and enabling real-time wireless environment adaptation.

Contribution

It presents a novel DT-driven RIS optimization method leveraging ray tracing, validated through experimental deployment on a physical RIS prototype.

Findings

DT-based phase configurations improve received signal power

Ray-tracing model accurately predicts physical environment behavior

Proposed method reduces channel estimation overhead

Abstract

Determining the optimal phase configurations of reconfigurable intelligent surface (RIS) elements typically requires complex channel estimation procedures with high pilot overhead, creating a bottleneck for real-time deployment in time-varying wireless environments. In this paper, we propose a digital twin (DT)-driven framework for RIS phase shift optimization that eliminates extensive signaling overhead associated with estimating high-dimensional RIS channels. Leveraging the NVIDIA Sionna ray-tracing library, we construct a DT of the physical environment based on a three-dimensional map. The proposed system utilizes the location information of the transceivers to compute the optimal RIS phase shift configurations within the DT. These computationally generated configurations are then transferred to a physical RIS prototype. Experimental results demonstrate that the phase configurations obtained from the DT significantly enhance the received signal power in the physical environment, validating the fidelity of the ray-tracing model and the feasibility of the proposed optimization strategy.