Decision-Making Large Language Model for Wireless Communication: A Comprehensive Survey on Key Techniques

Future 6G wireless systems will feature massive connectivity, complex tasks, limited resources, and heterogeneous architectures, posing significant challenges to decision-making regarding accuracy, generalization, and collaboration. Although large language models (LLMs) have demonstrated impressive capabilities in natural language processing and reasoning tasks, their application in wireless communication remains at a fragmented and exploratory stage. Given the potential of LLMs, this work provides a comprehensive overview of LLM-enabled decision-making. Firstly, in response to the lack of supervised annotations in communication scenarios, we summarize currently available open-source communication datasets and further explore LLM-based methods for data generation and augmentation, addressing a key gap in prior reviews on training data. We then analyze the evolution of LLM architectures for communication tasks, focusing on optimizations within the transformer architecture and emerging alternatives such as state space models and neuro-symbolic hybrid architectures. The discussion emphasizes their adaptability to challenges like dynamic channel modeling and edge deployment, providing a structural-level reference for selecting models in practical applications. Moreover, the paper provides a comprehensive analysis of the core techniques for decision-making. Unlike prior works that organize discussions around specific communication task types, we structure the reasoning process into five key dimensions: prompt learning, chain of thought reasoning, inference mechanisms, decision frameworks, and multi-agent coordination. This forms a task-adaptive decision-making pipeline aligned with the unique requirements of communication systems. Finally, we discuss deployment challenges and future directions, offering theoretical insights and practical guidance for the integration of LLMs into next-generation communication decision systems.