Large artificial intelligence models (LAMs) are transforming wireless physical layer technologies through their robust generalization, multitask processing, and multimodal capabilities. This article reviews recent advancements in applying LAMs to physical layer communications, addressing obstacles of conventional AI-based approaches. LAM-based solutions are classified into two strategies: leveraging pre-trained LAMs and developing native LAMs designed specifically for physical layer tasks. The motivations and key frameworks of these approaches are comprehensively examined through multiple use cases. Both strategies significantly improve performance and adaptability across diverse wireless scenarios. Future research directions, including efficient architectures, interpretability, standardized datasets, and collaboration between large and small models, are proposed to advance LAM-based physical layer solutions for next-generation communication systems.

翻译：大规模人工智能模型（LAMs）凭借其强大的泛化能力、多任务处理能力和多模态能力，正在变革无线物理层技术。本文综述了将LAMs应用于物理层通信的最新进展，并探讨了传统基于AI方法所面临的障碍。基于LAM的解决方案被归纳为两种策略：利用预训练的LAMs，以及开发专门为物理层任务设计的原生LAMs。本文通过多个应用案例，全面剖析了这些方法的动机与核心框架。两种策略均能显著提升多样化无线场景下的性能与适应性。为推进面向下一代通信系统的基于LAM的物理层解决方案，本文提出了未来的研究方向，包括高效架构、可解释性、标准化数据集以及大模型与小模型间的协同。