Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition

TL;DR

This paper introduces a diffusion-enhanced decision Transformer framework that improves channel state information acquisition and beamforming optimization in RIS-assisted wireless systems, enhancing efficiency and adaptability in dynamic environments.

Contribution

It proposes a novel diffusion model for rapid CSI acquisition and a decision Transformer for robust beamforming, with a fine-tuning mechanism for quick adaptation without retraining.

Findings

Enhanced CSI acquisition speed using diffusion models.

Improved beamforming robustness with decision Transformers.

Superior performance over state-of-the-art RL methods in simulations.

Abstract

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods.