大模型让无线物理层更智能，提升通信适应性与性能。
Large AI Models for Wireless Physical Layer
- 用预训练大模型或专为物理层设计的原生大模型解决传统AI方法瓶颈。
- 在多种无线场景中显著提升性能与适应能力，效果优于传统方法。
- 适合通信系统研究者、工程师及关注下一代智能通信的人群。
大规模人工智能模型（LAMs）正通过其强大的泛化能力、多任务处理和多模态特性，推动无线物理层技术变革。本文综述了近年来将LAMs应用于物理层通信的进展，分析了传统基于AI方法面临的挑战。现有方案分为两类：利用预训练LAMs和专为物理层任务设计的原生LAMs。通过多个应用场景，全面探讨了两类策略的动机与核心框架。两者均在多样化的无线环境中显著提升了性能与适应性。未来研究方向包括高效架构设计、可解释性增强、标准化数据集构建，以及大模型与小模型的协同机制，以推动下一代通信系统中基于LAMs的物理层解决方案发展。
原文摘要 · Abstract (English)
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.
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