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

2026-08-09

AI Summary

This study addresses the fragmentation in AI-native 6G wireless foundation model research by proposing the first unified taxonomy encompassing architectures, pretraining paradigms, and adaptation methods. By systematically integrating key techniques such as self-supervised learning, parameter-efficient fine-tuning, and cross-layer optimization, this work comprehensively reviews datasets and evaluation benchmarks to establish a holistic knowledge framework. Filling a critical gap in comprehensive surveys, it identifies core challenges in data representation and edge deployment. Consequently, this research provides an authoritative reference and systematic guidance for both theoretical investigation and engineering practice in next-generation general-purpose wireless intelligence systems, effectively bridging disparate research streams into a cohesive roadmap for future 6G development.

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.

Problem

• Wireless Foundation Models
• AI-Native 6G
• Unified Survey
• Transferable Intelligence

Innovation

• Wireless Foundation Models
• AI-Native 6G
• Self-Supervised Pre-training
• Parameter-Efficient Adaptation
• Unified Taxonomy