Dynamical ON-OFF Control with Trajectory Prediction for Multi-RIS Wireless Networks

TL;DR

This paper introduces a trajectory prediction-based dynamical ON-OFF control algorithm for RIS-assisted wireless networks, improving interference management and SINR by predicting user trajectories and adaptively controlling RIS states.

Contribution

The paper presents a novel TPC algorithm that integrates LSTM-based trajectory prediction with adaptive RIS ON-OFF control to enhance network performance.

Findings

TPC outperforms existing methods in simulation tests.

Adaptive RIS control improves SINR in large-scale networks.

Trajectory prediction effectively reduces interference.

Abstract

Reconfigurable intelligent surfaces (RISs) have demonstrated an unparalleled ability to reconfigure wireless environments by dynamically controlling the phase, amplitude, and polarization of impinging waves. However, as nearly passive reflective metasurfaces, RISs may not distinguish between desired and interference signals, which can lead to severe spectrum pollution and even affect performance negatively. In particular, in large-scale networks, the signal-to-interference-plus-noise ratio (SINR) at the receiving node can be degraded due to excessive interference reflected from the RIS. To overcome this fundamental limitation, we propose in this paper a trajectory prediction-based dynamical control algorithm (TPC) for anticipating RIS ON-OFF states sequence, integrating a long-short-term-memory (LSTM) scheme to predict user trajectories. In particular, through a codebook-based algorithm, the RIS controller adaptively coordinates the configuration of the RIS elements to maximize the received SINR. Our simulation results demonstrate the superiority of the proposed TPC method over various system settings.