Predictive control for RIS-aided B5G networks using Kalman filters RIS, Kalman filter, Predictive control, B5G, 6G

Rafael Marasca Martins, Luis Carlos Mathias, Taufik Abrão

DOI: 10.14209/sbrt.2025.1571150404

Evento: XLIII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2025)

Keywords:

Abstract

Reconfigurable Intelligent Surfaces (RIS) are anticipated to be a key enabling technology for beyond 5G (B5G) and 6G wireless networks, introducing the concept of a controllable radio environment. By dynamically shaping the propagation of electromagnetic waves, RIS can significantly enhance spectral efficiency and support communication in densely populated scenarios. However, one of the main challenges in deploying RIS lies in the design of effective beamforming control algorithms. In high-mobility scenarios, accurately and continuously tracking the user equipment (UE) position is essential to allow timely RIS reconfiguration and prevent signal degradation. When advanced sensing infrastructure is unavailable, the system must rely on low-rate and potentially unreliable GPS updates, which hinders real-time responsiveness. To address this, we propose a Kalman filter-based control algorithm that estimates the UE's position between GPS samples, enabling the RIS to adaptively optimize the received power. The results demonstrate that the Kalman filter approach can yield up to a fivefold improvement in the received power at the UE. This highlights the effectiveness of the proposed method as a low-complexity yet powerful solution for enabling real-time beamforming in mobile scenarios with sparse location measurements.

Download