Trajectory-Aware Multi-RIS Activation and Configuration: A Riemannian Diffusion Method

TL;DR

This paper introduces a trajectory-aware control framework for multi-RIS systems that predicts user movement and interference to optimize RIS activation and phase configuration, significantly improving wireless signal quality.

Contribution

It proposes a novel Riemannian diffusion model guided by reinforcement learning for phase configuration and a trajectory prediction method for multi-RIS control, addressing nonconvex optimization challenges.

Findings

Achieves up to 30% SINR improvement over learning-based control

Realizes up to 44% gain compared to RIS always-on scheme

Outperforms state-of-the-art baselines across various scenarios

Abstract

Reconfigurable intelligent surfaces (RISs) offer a low-cost, energy-efficient means for enhancing wireless coverage. Yet, their inherently programmable reflections may unintentionally amplify interference, particularly in large-scale, multi-RIS-enabled mobile communication scenarios where dense user mobility and frequent line-of-sight overlaps can severely degrade the signal-to-interference-plus-noise ratio (SINR). To address this challenge, this paper presents a novel generative multi-RIS control framework that jointly optimizes the ON/OFF activation patterns of multiple RISs in the smart wireless environment and the phase configurations of the activated RISs based on predictions of multi-user trajectories and interference patterns. We specially design a long short-term memory (LSTM) artificial neural network, enriched with speed and heading features, to forecast multi-user trajectories, thereby enabling reconstruction of future channel state information. To overcome the highly nonconvex nature of the multi-RIS control problem, we develop a Riemannian diffusion model on the torus to generate geometry-consistent phase-configuration, where the reverse diffusion process is dynamically guided by reinforcement learning. We then rigorously derive the optimal ON/OFF states of the metasurfaces by comparing predicted achievable rates under RIS activation and deactivation conditions. Extensive simulations demonstrate that the proposed framework achieves up to 30\% SINR improvement over learning-based control and up to 44\% gain compared with the RIS always-on scheme, while consistently outperforming state-of-the-art baselines across different transmit powers, RIS configurations, and interference densities.