Trajectory-Aware Multi-RIS Activation and Configuration: A Riemannian Diffusion Method | Kaining Wang et al. | ResearchPod

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

Alex: Welcome to another episode of ResearchPod.

Sam: Today we're looking at a paper titled "Trajectory-Aware Multi-RIS Activation and Configuration: A Riemannian Diffusion Method." It addresses a problem in next-generation wireless networks. These use special panels called reconfigurable intelligent surfaces, or RIS, that work like adjustable mirrors for radio waves. They bounce signals to extend coverage with little power. But in crowded places like marathons, where runners send health data from wearables, moving people create interference. The panels can bounce that unwanted noise too, muddling the key signals.

Alex: So the panels extend range but worsen interference from all those moving devices?

Sam: Yes. Runners and spectators pack together, their devices overlap on frequencies, and panels reflect everything—signals and noise. This lowers the signal-to-interference-plus-noise ratio, or SINR, a measure of how clear the desired signal stands out from the mess. The paper's system predicts user paths ahead, then decides for each panel whether to turn it on or off, and how to angle its reflections. That boosts good signals and blocks bad ones.

Alex: It's about smart choices, not always using the panels.

Sam: Exactly. First, it guesses future paths to spot interference risks. Then it uses a math tool on a looped shape—like a donut—for panel angles, keeping them valid. It checks if turning a panel on helps data rates more than leaving it off. Simulations with real movement data show it works about 30% better than other smart methods and 44% better than always-on panels.

Alex: How does the path prediction work—what helps it guess where runners head next?

Sam: It tracks recent positions, like dots on a path map, plus speed and direction from position changes. A network called an LSTM remembers patterns from past walks, like recalling turns on a familiar route, trained on real data from Beijing streets. It forecasts spots a few steps ahead, then rebuilds future signal paths from base station to panels to users.

Alex: Adding speed and direction handles turns in crowds.

Sam: Yes. Those predictions feed into setting angles for clearer signals. For on-off, it calculates data speeds: one with the panel tuned and active, one direct without it. Activate only if active is better—avoiding interference boosts. Simulations show 44% better results than always-on setups.

Alex: Turning some off—doesn't that weaken the overall signal?

Sam: Not in dense spots. Deactivating harmful panels prevents them from adding noise, letting helpful ones work better. The base station coordinates centrally, like a coach choosing players. Tests with marathon paths gave 30% clearer SINR than other methods by avoiding predicted overlaps.

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

Kaining Wang, Bo Yang, Yusheng Lei, Zhibo Li, Zhiwen Yu, Xuelin Cao, Bin Guo, George C. Alexandropoulos, Dusit Niyato, Mérouane Debbah, Zhu Han

Feb 8, 2026·6:52·arxiv·Original Paper

Reconfigurable Intelligent Surfaces (RISs)Multi-RIS Activation ControlTrajectory PredictionRiemannian Diffusion ModelInterference Mitigation in 6GReconfigurable Intelligent Surface (RIS)

Listen

7 min

Briefing

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.

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

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

Listen