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

TL;DR

The paper tackles interference amplification in dense multi-RIS uplink networks with high mobility by proposing a trajectory-predicted generative ON/OFF control framework (TPGC). It combines an LSTM-based trajectory predictor to reconstruct future CSI with a Riemannian diffusion model on the torus, guided by reinforcement learning, to generate geometry-consistent RIS phase configurations and selective ON/OFF activation. The approach yields up to 30% SINR improvement over learning-based controls and up to 44% gains over always-on RIS schemes, with strong mobility robustness and generalization across interference densities. This work enables proactive, geometry-aware RIS control for reliable multi-RIS communications in large-scale, dynamic environments such as outdoor events and crowded venues.

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.

Figures (15)

• Figure 1: On-site deployment of a 3.5 GHz RIS during the Beijing Changping Yanshou Trail Challenge 2025 beijing2025news, providing enhanced wireless coverage in marathon scenarios.
• Figure 2: The RIS reflects both desired and interfering signals, resulting in potential performance degradation. With the TPGC framework, BS predicts user trajectories and dynamically manages RIS ON/OFF states, enhancing signal quality and minimizing interference in rapidly changing dense mobile environments.
• Figure 3: The proposed RIS unit cell for ON/OFF states. The left figure is the RIS structure with copper on an F4B substrate and embedded PIN diodes. The right figure shows the corresponding equivalent diode circuits under different PIN bias currents.
• Figure 4: The proposed TPGC structure. A trajectory prediction module forecasts future user locations, which are then used to predict the CSI of the cascaded channels. Conditioned on the predicted CSI, the proposed RDM-based DRL optimization module outputs the RIS ON/OFF decisions and passive beamforming to maximize the communication performance.
• Figure 5: Architecture of the proposed RDM–DRL framework for trajectory-predicted RIS phase and ON/OFF control. The framework takes the predicted cascaded CSI, reconstructed from future user trajectories, as the conditioning observation. The RDM is employed as a generative actor that samples RIS phase configurations directly on the torus manifold. The reverse diffusion process is guided by a TD3-based critic, which evaluates the achievable rate and steers the denoising trajectory toward high-quality phase solutions. After obtaining the optimized continuous phase configuration, the achievable rates under RIS activation and deactivation are compared to determine the final ON/OFF control decision.