An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models

TL;DR

This paper reviews machine learning techniques, especially reinforcement learning and large language models, for optimizing reconfigurable intelligent surfaces in 6G networks, highlighting new integration opportunities and future challenges.

Contribution

It introduces the novel concept of combining large language models with reinforcement learning for RIS optimization in 6G networks, expanding beyond traditional methods.

Findings

LLMs can enhance RL in network optimization

Different RL techniques are applicable to RIS management

Future challenges include integration and scalability

Abstract

Reconfigurable intelligent surface (RIS) becomes a promising technique for 6G networks by reshaping signal propagation in smart radio environments. However, it also leads to significant complexity for network management due to the large number of elements and dedicated phase-shift optimization. In this work, we provide an overview of machine learning (ML)-enabled optimization for RIS-aided 6G networks. In particular, we focus on various reinforcement learning (RL) techniques, e.g., deep Q-learning, multi-agent reinforcement learning, transfer reinforcement learning, hierarchical reinforcement learning, and offline reinforcement learning. Different from existing studies, this work further discusses how large language models (LLMs) can be combined with RL to handle network optimization problems. It shows that LLM offers new opportunities to enhance the capabilities of RL algorithms in terms of generalization, reward function design, multi-modal information processing, etc. Finally, we identify the future challenges and directions of ML-enabled optimization for RIS-aided 6G networks.