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 core 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 of the future.

翻译：大型语言模型（LLMs）的出现彻底改变了人工智能领域，其在推理、泛化和零样本学习方面展现出前所未有的能力。这些优势为无线通信开辟了新的前沿，因为日益增长的复杂性和动态性亟需智能且自适应的解决方案。本文探讨了LLMs在无线系统转型中的三个关键方向：将预训练的LLMs适配于核心通信任务，开发兼顾通用性与效率的无线专用基础模型，以及赋予LLMs具备自主推理与协调能力的智能体特性。我们重点介绍了基于LLM方法相较于传统方法的近期进展、实际案例研究及其独特优势。最后，我们概述了开放挑战与研究机遇，包括多模态融合、与轻量级模型的协作以及自我改进能力，从而为未来智能、自适应且自主的无线网络规划发展路径。