The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed, configured, and managed. Recent advancements in Large Language Models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. However, existing studies on LLMs for wireless systems are limited to a direct application for telecom language understanding. To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks. We first identify three foundational principles that underpin WirelessLLM: knowledge alignment, knowledge fusion, and knowledge evolution. Then, we investigate the enabling technologies to build WirelessLLM, including prompt engineering, retrieval augmented generation, tool usage, multi-modal pre-training, and domain-specific fine-tuning. Moreover, we present three case studies to demonstrate the practical applicability and benefits of WirelessLLM for solving typical problems in wireless networks. Finally, we conclude this paper by highlighting key challenges and outlining potential avenues for future research.

翻译：无线技术的快速演进和网络基础设施日益增长的复杂性，迫切需要通信网络在设计、配置和管理方式上实现范式转变。大型语言模型（LLMs）的最新进展激发了人们对其革新无线通信系统潜力的兴趣。然而，现有针对无线系统的LLM研究仅限于电信语言理解的直接应用。为了赋予LLMs在无线领域的知识和专业能力，本文提出WirelessLLM，这是一个用于适配和增强LLMs以应对无线通信网络独特挑战与需求的综合性框架。我们首先确立了支撑WirelessLLM的三个基本原则：知识对齐、知识融合与知识演进。随后，我们探讨了构建WirelessLLM的使能技术，包括提示工程、检索增强生成、工具使用、多模态预训练和领域特定微调。此外，我们通过三个案例研究展示了WirelessLLM在解决无线网络中典型问题时的实际适用性与优势。最后，本文总结了关键挑战并展望了未来研究的潜在方向。