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

• URL: http://arxiv.org/abs/2608.14694v1
• Date: Sun, 09 Aug 2026 19:24:26 GMT
• Title: A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks
• Abstract summary: Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks.<n>WFMs learn generalized representations from large-scale heterogeneous wireless data.<n>WFMs can be efficiently adapted to communication, sensing, localization, and network optimization tasks.
• Score: 5.080645187815144
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Wireless Foundation Models: State-of-the-Art and Open Challenges [8.033625328567284]
  Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations from large-scale wireless data.<n>This survey provides a systematic analysis of WFMs for physical-layer applications.
  arXiv  Detail & Related papers  (2026-09-04T04:26:04Z)
• Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey [64.20429885919701]
  We propose an energy accounting framework identifying computational and communication costs across the Perception-Reasoning-Action cycle.<n>We establish a unified taxonomy spanning model simplification, computation control, input and attention optimization, and hardware-aware inference.<n>We identify open challenges of federated green learning, carbon-aware agency, 6th generation mobile communication (6G)-native Agentic AI, and self-sustaining systems.
  arXiv  Detail & Related papers  (2026-04-09T06:13:59Z)
• A Graph Foundation Model for Wireless Resource Allocation [37.503398874234094]
  We propose a graph foundation model for resource allocation based on a pre-training and fine-tuning paradigm to extract unified representations.<n>Specifically, we introduce an interference-aware Transformer architecture with a bias projector that injects interference topologies into global attention mechanisms.<n>Experiments demonstrate that the proposed framework achieves state-of-the-art performance and scales effectively with increased model capacity.
  arXiv  Detail & Related papers  (2026-04-08T06:07:28Z)
• Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications [60.721304295812445]
  Federated learning (FL) has the potential to improve the overall loop of agentic AI.<n>We first summarize fundamentals of agentic AI and mainstream FL types. Then, we illustrate how each FL type can strengthen a specific component of agentic AI's loop.<n>We conduct a case study on using FRL to improve the performance of agentic AI's action decision in low-altitude wireless networks.
  arXiv  Detail & Related papers  (2026-03-02T11:26:56Z)
• A Multi-Modal Foundational Model for Wireless Communication and Sensing [5.101849923596286]
  This work introduces a task-agnostic, multi-modal foundational model for physical-layer wireless systems.<n>It learns transferable, physics-aware representations across heterogeneous modalities, enabling robust generalization across tasks and environments.<n>Our evaluations demonstrate superior generalization, robustness to deployment shifts, and reduced data requirements compared to task-specific baselines.
  arXiv  Detail & Related papers  (2026-02-03T21:03:23Z)
• AI-Native Integrated Sensing and Communications for Self-Organizing Wireless Networks: Architectures, Learning Paradigms, and System-Level Design [0.0]
  Integrated Sensing and Communications (ISAC) is emerging as a foundational paradigm for next-generation wireless networks.<n>By tightly coupling radio sensing with communication functions, ISAC unlocks new capabilities for situational awareness, localization, tracking, and network adaptation.<n>This survey provides a comprehensive and system-level review of AI-native ISAC-enabled self-organizing wireless networks.
  arXiv  Detail & Related papers  (2025-12-29T05:45:57Z)
• A Survey on Generative Recommendation: Data, Model, and Tasks [55.36322811257545]
  generative recommendation reconceptualizes recommendation as a generation task rather than discriminative scoring.<n>This survey provides a comprehensive examination through a unified tripartite framework spanning data, model, and task dimensions.<n>We identify five key advantages: world knowledge integration, natural language understanding, reasoning capabilities, scaling laws, and creative generation.
  arXiv  Detail & Related papers  (2025-10-31T04:02:58Z)
• Diffusion Models for Future Networks and Communications: A Comprehensive Survey [65.97057929688499]
  The rise of Generative AI (GenAI) in recent years has catalyzed transformative advances in wireless communications and networks.<n>Among the members of the GenAI family, Diffusion Models (DMs) have risen to prominence as a powerful option.<n>We aim to provide a comprehensive overview of the theoretical foundations and practical applications of DMs across future communication systems.
  arXiv  Detail & Related papers  (2025-08-03T04:59:58Z)
• World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks [55.90051810762702]
  We present a comprehensive overview of world models, highlighting their architecture, training paradigms, and applications across prediction, generation, planning, and causal reasoning.<n>We propose Wireless Dreamer, a novel world model-based reinforcement learning framework tailored for wireless edge intelligence optimization.
  arXiv  Detail & Related papers  (2025-05-31T06:43:00Z)
• Learning Wireless Data Knowledge Graph for Green Intelligent Communications: Methodology and Experiments [36.82287751359216]
  We propose a pervasive multi-level (PML) native AI architecture, which integrates the concept of knowledge graph (KG) into the intelligent operational manipulations of mobile networks. We characterize the massive and complex data collected from wireless communication networks and analyze the relationships among various data fields. This architecture not only enhances AI training, inference, and validation processes but also significantly reduces resource wastage and overhead for communication networks.
  arXiv  Detail & Related papers  (2024-04-16T07:55:34Z)
• Toward 6G Native-AI Network: Foundation Model based Cloud-Edge-End Collaboration Framework [55.73948386625618]
  We analyze the challenges of achieving 6G native AI from perspectives of data, AI models, and operational paradigm.<n>We propose a 6G native AI framework based on foundation models, provide an integration method for the expert knowledge, present the customization for two kinds of PFM, and outline a novel operational paradigm for the native AI framework.
  arXiv  Detail & Related papers  (2023-10-26T15:19:40Z)
• An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization [3.5573601621032935]
  This paper focuses on demonstrating a working system prototype of the 5G Core (5GC) network and the Network Data Analytics Function (NWDAF) used to bring the benefits of data-driven techniques to fruition. Analyses of the network-generated data explore core intra-network interactions through unsupervised learning, clustering, and evaluate these results as insights for future opportunities and works.
  arXiv  Detail & Related papers  (2022-09-21T15:21:59Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.