Decision Transformer for IRS-Assisted Systems with Diffusion-Driven Generative Channels

TL;DR

This paper introduces a diffusion-decision transformer architecture that improves beamforming in IRS-assisted MISO systems by reducing channel estimation complexity and enhancing adaptability to changing channel conditions, achieving faster convergence and higher data rates.

Contribution

The paper presents a novel diffusion-decision transformer framework that combines diffusion models for channel estimation with offline pre-training and online fine-tuning for robust beamforming in IRS-assisted systems.

Findings

Boosts convergence speed by 3 times compared to RL

Enhances average user data rate by 6%

Reduces need for retraining in changing channel environments

Abstract

In this paper, we propose a novel diffusion-decision transformer (D2T) architecture to optimize the beamforming strategies for intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) communication systems. The first challenge lies in the expensive computation cost to recover the real-time channel state information (CSI) from the received pilot signals, which usually requires prior knowledge of the channel distributions. To reduce the channel estimation complexity, we adopt a diffusion model to automatically learn the mapping between the received pilot signals and channel matrices in a model-free manner. The second challenge is that, the traditional optimization or reinforcement learning (RL) algorithms cannot guarantee the optimality of the beamforming policies once the channel distribution changes, and it is costly to resolve the optimized strategies. To enhance the generality of the decision models over varying channel distributions, we propose an offline pre-training and online fine-tuning decision transformer (DT) framework, wherein we first pre-train the DT offline with the data samples collected by the RL algorithms under diverse channel distributions, and then fine-tune the DT online with few-shot samples under a new channel distribution for a generalization purpose. Simulation results demonstrate that, compared with retraining RL algorithms, the proposed D2T algorithm boosts the convergence speed by 3 times with only a few samples from the new channel distribution while enhancing the average user data rate by 6%.