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

2026-02-08

AI Summary

This work addresses the degradation of signal-to-interference-plus-noise ratio (SINR) and increased interference caused by user mobility in multi-reconfigurable intelligent surface (RIS) scenarios. To tackle this, the authors propose a collaborative control framework integrating trajectory prediction, Riemannian diffusion generation, and reinforcement learning guidance. Specifically, an LSTM-based module predicts user trajectories—including velocity and heading—and a Riemannian diffusion model is constructed on a torus manifold to capture the geometric structure of phase configurations. The reverse diffusion process is steered by a reinforcement learning agent to yield dynamic, geometry-consistent, and interference-aware RIS phase shifts. Additionally, RIS on/off states are adaptively determined based on achievable rate comparisons. Experimental results demonstrate that the proposed approach improves SINR by up to 30% over existing learning-based methods and by 44% compared to always-on RIS schemes, consistently outperforming baselines across varying transmit powers, RIS configurations, and interference densities.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Reconfigurable Intelligent Surfaces
• Interference Amplification
• Mobile Communication
• SINR Degradation
• Multi-RIS Systems

Innovation

• Riemannian diffusion
• multi-RIS activation
• trajectory prediction
• phase configuration
• reinforcement learning