Chain-of-Thought for Large Language Model-empowered Wireless Communications

TL;DR

This paper explores the use of Chain-of-Thought prompting with large language models to enhance reasoning and decision-making in wireless communications, demonstrated through a UAV network case study.

Contribution

It introduces a multi-layer intent-driven CoT framework that links natural language user intent to wireless control actions using reinforcement learning.

Findings

Framework outperforms non-CoT baseline in communication performance

Significantly improves reasoning interpretability in wireless control

Demonstrates effectiveness in UAV network case study

Abstract

Recent advances in large language models (LLMs) have opened new possibilities for automated reasoning and decision-making in wireless networks. However, applying LLMs to wireless communications presents challenges such as limited capability in handling complex logic, generalization, and reasoning. Chain-of-Thought (CoT) prompting, which guides LLMs to generate explicit intermediate reasoning steps, has been shown to significantly improve LLM performance on complex tasks. Inspired by this, this paper explores the application potential of CoT-enhanced LLMs in wireless communications. Specifically, we first review the fundamental theory of CoT and summarize various types of CoT. We then survey key CoT and LLM techniques relevant to wireless communication and networking. Moreover, we introduce a multi-layer intent-driven CoT framework that bridges high-level user intent expressed in natural language with concrete wireless control actions. Our proposed framework sequentially parses and clusters intent, selects appropriate CoT reasoning modules via reinforcement learning, then generates interpretable control policies for system configuration. Using the unmanned aerial vehicle (UAV) network as a case study, we demonstrate that the proposed framework significantly outperforms a non-CoT baseline in both communication performance and quality of generated reasoning.