Show Me the Way: Real-Time Tracking of Wireless Mobile Users with UWB-Enabled RIS

TL;DR

The paper tackles real-time RIS beam steering in indoor settings with mobile users by leveraging UWB-based localization to infer geometry and drive a model-based RIS configuration. It introduces two robustness techniques—beam splitting and momentum-like UWB estimation correction—to mitigate UWB fluctuations and improve tracking reliability. Through extensive simulations and indoor experiments, the authors show that these methods substantially improve worst-case tracking performance, achieving up to 17.5 dB gains when combined. The work advances practical RIS deployment by addressing real-time geometry estimation, near-field beamforming, and robustness to localization noise, enabling more reliable RIS-assisted communication in dynamic environments.

Abstract

The integration of Reconfigurable Intelligent Surfaces (RIS) in 6G wireless networks offers unprecedented control over communication environments. However, identifying optimal configurations within practical constraints remains a significant challenge. This becomes especially pronounced, when the user is mobile and the configurations need to be deployed in real time. Leveraging Ultra-Wideband (UWB) as localization technique, we capture and analyze real-time movements of a user within the RIS-enabled indoor environment. Given this information about the system's geometry, a model-based optimization is utilized, which enables real-time beam steering of the RIS towards the user. However, practical limitations of UWB modules lead to fluctuating UWB estimates, causing the RIS beam to occasionally miss the tracked user. The methodologies proposed in this work aim to increase the compatibility between these two systems. To this end, we provide two key solutions: beam splitting for obtaining more robust RIS configurations and UWB estimation correction for reducing the variations in the UWB data. Through comprehensive theoretical and experimental evaluations in both stationary and mobile scenarios, the effectiveness of the proposed techniques is demonstrated. When combined, the proposed methods improve worst-case tracking performance by a significant 17.5dB compared to the conventional approach.

Figures (12)

• Figure 1: RIS and antennas in an indoor office environment.
• Figure 2: The logarithmic magnitude of the channel values at $\{d^{\mathsf{UWB}},\nu^\mathsf{UWB}\}$ for each possible combination during phase matching procedure using the DSM with $\tilde{d}^\mathsf{UWB}=0.1$ m and ASM with $\tilde{\nu}^\mathsf{UWB}=2.5^\circ$ and $T=8$.
• Figure 3: Virtual representation of the experimental setup. The arrow indicates the direction of the trajectory during the simulations.
• Figure 4: The logarithmic magnitude of the channel values at $\{d^{\mathsf{UWB}},\nu^\mathsf{UWB}\}$ simulated for the conventional optimization as well as using the ASM with $\tilde{d}^\mathsf{UWB}=0.1$ m and DSM with $\tilde{\nu}^\mathsf{UWB}=2.5^\circ$ for $T=\{1,2,4\}$.
• Figure 5: The logarithmic magnitude of the channel values at $\{d^{\mathsf{UWB}},\nu^\mathsf{UWB}\}$ simulated for the conventional optimization as well as using DSM with $\tilde{\nu}^\mathsf{UWB}=2.5^\circ$ for $T=\{1,2,4\}$.