用大模型让无线网络更智能，从适配到自主决策
Large Language Models for Wireless Communications: From Adaptation to Autonomy
- 将通用大模型改造用于通信任务，提升适应性
- 构建专用基础模型，在效率与能力间取得平衡
- 实现自主推理与协同，推动网络向智能进化
大语言模型（LLMs）的兴起正重塑人工智能，其强大的推理、泛化与零样本学习能力为无线通信带来了新机遇。面对日益复杂和动态的系统需求，本文探讨了大模型在三个关键方向的潜力：将预训练大模型适配至通信任务；开发兼顾通用性与效率的无线专用基础模型；以及构建具备自主推理与协同能力的代理型大模型。文章总结了近期进展、实际案例，并强调基于大模型的方法相较于传统技术的优势。最后，指出了多模态融合、与轻量模型协作、自我进化等开放挑战与研究机遇，为迈向智能、自适应、自治的无线网络铺平道路。
原文摘要 · Abstract (English)
The emergence of large language models (LLMs) has revolutionized artificial intelligence, offering unprecedented capabilities in reasoning, generalization, and zero-shot learning. These strengths open new frontiers in wireless communications, where increasing complexity and dynamics demand intelligent and adaptive solutions. This article explores the role of LLMs in transforming wireless systems across three key directions: adapting pretrained LLMs for communication tasks, developing wireless-specific foundation models to balance versatility and efficiency, and enabling agentic LLMs with autonomous reasoning and coordination capabilities. We highlight recent advances, practical case studies, and the unique benefits of LLM-based approaches over traditional methods. Finally, we outline open challenges and research opportunities, including multimodal fusion, collaboration with lightweight models, and self-improving capabilities, charting a path toward intelligent, adaptive, and autonomous wireless networks.
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