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

TL;DR

This work investigates chain-of-thought prompting to enhance large language models for wireless communications, addressing interpretability and multi-step decision-making in dynamic networks. It provides a comprehensive overview of CoT principles and a taxonomy of techniques, then surveys CoT-enabled wireless applications such as path planning, resource allocation, semantic communications, and ISAC. A multi-layer intent-driven CoT framework is proposed to translate natural-language intents into executable wireless policies, demonstrated via a UAV deployment and power-control case study that shows substantial gains over non-CoT baselines. The results highlight CoT’s potential to improve reliability, efficiency, and transparency in real-time wireless optimization, with future directions including enhanced interpretability and edge-based federated reasoning.

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.

Figures (5)

• Figure 1: An example to illustrate the different chain-of-thought techniques. Zero-shot CoT generates reasoning chains without examples. Few-shot CoT provides exemplars to guide intermediate steps. Self-consistency CoT samples multiple reasoning paths and selects the most consistent answer. Auto-CoT automatically generates exemplars for prompting. Tree-of-Thought explores multiple reasoning branches for better decisions. Graph-of-Thought models reasoning as a revisitable graph structure.
• Figure 2: The principles, advantages, disadvantages, and suitable applications of different chain-of-thought strategies, including Zero-shot CoT, Few-shot CoT, Auto-CoT, Self-consistency CoT, Tree-of-Thought, and Graph-of-Thought. Related references [A1-A11] and [B1-B4] can be found in https://github.com/wang104225/CoT_Wireless.
• Figure 3: The proposed CoT-enabled IDNs framework operates across three layers: the application layer, the Chain-of-Thought-enabled decision layer, and the infrastructure layer. In the application layer, diverse user intents and environmental information are collected and encoded. In the Chain-of-Thought-enabled decision layer, intent understanding and decision generation are achieved through Steps 1–5. In addition, a case of Auto-CoT reasoning chain construction is presented. In the infrastructure layer, the generated policies are then executed in real-world network environments.
• Figure 4: Coverage rate and sum rate performance of the proposed framework versus the communication range of UAV under two configurations: GPT-4o with CoT (GPT-4o-w-CoT), and GPT-4o without CoT (GPT-4o-w/o-CoT), and all employ DRL-enhanced module activation.
• Figure 5: Comprehensive performance comparison of the proposed CoT-driven framework under three configurations: CoT-enhanced GPT-4o with DRL activation (GPT-4o-DRL), CoT-enhanced GPT-3.5 with DRL activation (GPT-3.5-DRL), and CoT-enhanced GPT-3.5 with random activation (GPT-3.5-Random).