Beamforming Control in RIS-Aided Wireless Communications: A Predictive Physics-Based Approach

TL;DR

This paper introduces a physics-based predictive control method for RIS beamforming in wireless systems, enabling proactive adjustments based on estimated future user positions to mitigate latency issues.

Contribution

It presents a scalable kinematic observer and predictor that improve RIS control by forecasting user movement, addressing latency and update rate limitations in dynamic environments.

Findings

Effective real-time RIS adjustments achieved

Low computational complexity demonstrated

Robust performance across various user mobility models

Abstract

Integrating reconfigurable intelligent surfaces (RIS) into wireless communication systems is a promising approach for enhancing coverage and data rates by intelligently redirecting signals, through a process known as beamforming. However, the process of RIS beamforming (or passive beamforming) control is associated with multiple latency-inducing factors. As a result, by the time the beamforming is effectively updated, the channel conditions may have already changed. For example, the low update rate of localization systems becomes a critical limitation, as a mobile UE's position may change significantly between two consecutive measurements. To address this issue, this work proposes a practical and scalable physics-based solution that is effective across a wide range of UE movement models. Specifically, we propose a kinematic observer and predictor to enable proactive RIS control. From low-rate position estimates provided by a localizer, the kinematic observer infers the UE's speed and acceleration. These motion parameters are then used by a predictor to estimate the UE's future positions at a higher rate, allowing the RIS to adjust promptly and compensate for inherent delays in both the RIS control and localization systems. Numerical results validate the effectiveness of the proposed approach, demonstrating real-time RIS adjustments with low computational complexity, even in scenarios involving rapid UE movement.