Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication

TL;DR

This paper introduces a novel framework combining foundation models and deep reinforcement learning to optimize RIS-assisted wireless communication, significantly improving spectral efficiency with reduced channel estimation overhead.

Contribution

It proposes using a pre-trained foundation model to generate channel embeddings for joint optimization of beamforming and RIS configurations, enhancing decision-making in wireless networks.

Findings

Achieves 9.89% higher spectral efficiency than traditional DRL methods.

Outperforms beam sweeping-based approaches by 43.66% in spectral efficiency.

Demonstrates effective integration of foundation models with DRL for network optimization.

Abstract

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by dynamically controlling signal propagation in the environment. However, their efficient deployment relies on accurate channel state information (CSI), which leads to high channel estimation overhead due to their passive nature and the large number of reflective elements. In this work, we solve this challenge by proposing a novel framework that leverages a pre-trained open-source foundation model (FM) named large wireless model (LWM) to process wireless channels and generate versatile and contextualized channel embeddings. These embeddings are then used for the joint optimization of the BS beamforming and RIS configurations. To be more specific, for joint optimization, we design a deep reinforcement learning (DRL) model to automatically select the BS beamforming vector and RIS phase-shift matrix, aiming to maximize the spectral efficiency (SE). This work shows that a pre-trained FM for radio signal understanding can be fine-tuned and integrated with DRL for effective decision-making in wireless networks. It highlights the potential of modality-specific FMs in real-world network optimization. According to the simulation results, the proposed method outperforms the DRL-based approach and beam sweeping-based approach, achieving 9.89% and 43.66% higher SE, respectively.