Tutorial on Large Language Model-Enhanced Reinforcement Learning for Wireless Networks

TL;DR

This tutorial explores how Large Language Models can enhance Reinforcement Learning in wireless networks by improving generalization, interpretability, and efficiency, through a comprehensive taxonomy, review, and case studies.

Contribution

It introduces a novel taxonomy categorizing LLM roles in RL for wireless networks and reviews existing studies and applications in various network scenarios.

Findings

LLMs can serve as state perceivers, reward designers, decision-makers, and generators in RL.

Case studies demonstrate improved performance in low-altitude economy, vehicular, and space-air-ground networks.

The paper discusses future directions for LLM-enhanced RL in wireless communications.

Abstract

Reinforcement Learning (RL) has shown remarkable success in enabling adaptive and data-driven optimization for various applications in wireless networks. However, classical RL suffers from limitations in generalization, learning feedback, interpretability, and sample efficiency in dynamic wireless environments. Large Language Models (LLMs) have emerged as a transformative Artificial Intelligence (AI) paradigm with exceptional capabilities in knowledge generalization, contextual reasoning, and interactive generation, which have demonstrated strong potential to enhance classical RL. This paper serves as a comprehensive tutorial on LLM-enhanced RL for wireless networks. We propose a taxonomy to categorize the roles of LLMs into four critical functions: state perceiver, reward designer, decision-maker, and generator. Then, we review existing studies exploring how each role of LLMs enhances different stages of the RL pipeline. Moreover, we provide a series of case studies to illustrate how to design and apply LLM-enhanced RL in low-altitude economy networking, vehicular networks, and space-air-ground integrated networks. Finally, we conclude with a discussion on potential future directions for LLM-enhanced RL and offer insights into its future development in wireless networks.