A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks

[2608.14694] A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.14694 (cs) [Submitted on 9 Aug 2026] Title: A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks Authors: Naveed Khan , Besan Al Sbeihi , Maryam Alshehhi , Nasir Saeed View a PDF of the paper titled A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks, by Naveed Khan and 3 other authors View PDF HTML (experimental) Abstract: 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. Comments: 28 Pages, submitted to IEEE Communications Surveys and Tutorials Subjects: Artificial Intelligence (cs.AI) ; Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP) Cite as: arXiv:2608.14694 [cs.AI] (or arXiv:2608.14694v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14694 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Nasir Saeed [ view email ] [v1] Sun, 9 Aug 2026 19:24:26 UTC (8,818 KB) Full-text links: Access Paper: View a PDF of the paper titled A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks, by Naveed Khan and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.NI eess eess.SP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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