面向AI原生6G网络的无线基础模型综合综述
A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks
- College of Engineering, United Arab Emirates University（阿联酋大学工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该综述针对无线基础模型（WFMs）的设计、学习与部署展开统一梳理，明确其概念与分类，综述相关架构、方法及应用，分析关键挑战并展望未来方向，为AI原生6G网络智能系统研发提供参考。
AI中文摘要：
基础模型正成为AI原生第六代（6G）无线网络的变革性范式，它能在各类通信任务中实现可扩展、可迁移且数据高效的智能。与针对单一应用训练的传统深度学习模型不同，无线基础模型（Wireless Foundation Models, WFMs）从大规模异构无线数据中学习通用表示，可通过极少的特定任务监督高效适配到通信、感知、定位及网络优化任务。尽管研究进展迅速，但当前研究在架构、训练范式和应用领域上仍呈碎片化状态，尚无针对WFMs设计、学习与部署的统一综述。本综述对无线基础模型进行了全面且统一的梳理：首先明确WFMs的基本概念，并引入按模型架构、预训练范式及应用划分的分类体系；随后综述代表性架构、自监督预训练策略、参数高效适配方法、数据集、基准及评估方法，强调其在实现可迁移无线智能中的作用；此外，探讨了涵盖物理层信号处理、网络智能及跨层优化的新兴应用，并讨论了数据可用性、泛化性、可解释性、高效边缘部署及标准化等关键挑战；最后，展望了面向AI原生6G网络的可扩展、可信及通用无线智能的未来研究方向。本综述为开发下一代智能无线系统的研究人员和从业者提供了全面参考。
英文摘要：
Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks. Unlike conventional deep learning models that are trained for individual applications, wireless foundation models (WFMs) learn generalized representations from large-scale heterogeneous wireless data and can be efficiently adapted to communication, sensing, localization, and network optimization tasks with minimal task-specific supervision. Despite rapid progress, current research remains fragmented across architectures, training paradigms, and application domains, with no unified survey dedicated to the design, learning, and deployment of WFMs. This survey presents a comprehensive and unified review of wireless foundation models. We first establish the fundamental concepts of WFMs and introduce a taxonomy that organizes the field according to model architectures, pre-training paradigms, and applications. We then review representative architectures, self-supervised pre-training strategies, parameter-efficient adaptation methods, datasets, benchmarks, and evaluation methodologies, highlighting their roles in enabling transferable wireless intelligence. Furthermore, we examine emerging applications spanning physical-layer signal processing, network intelligence, and cross-layer optimization, and discuss the key challenges of data availability, generalization, interpretability, efficient edge deployment, and standardization. Finally, we outline future research directions toward scalable, trustworthy, and general-purpose wireless intelligence for AI-native 6G networks. This survey provides a comprehensive reference for researchers and practitioners developing next-generation intelligent wireless systems.
