Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy
Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Hong Kong University of Science and Technology（香港科技大学）；Yonsei University（延世大学）；MediaTek（联发科）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；Imperial College（帝国理工学院）
Le Liang, Jiajia Guo, Jun Zhang, Chan-Byoung Chae, Lu Lu, Shugong Xu, Octavia A. Dobre, Shi Jin, Geoffrey Ye Li
6G networks will introduce unprecedented complexity, which calls for a paradigm shift in network optimization and management. Artificial intelligence (AI)-based solutions, especially those enabled by the recently developed foundation models, have been recognized as promising candidates. Foundation models are large-scale AI models with general-purpose feature extraction capabilities, and once trained on massive amounts of data, they can be adapted to solve a wide range of downstream tasks, either in a zero-shot manner or with few-shot fine-tuning. This article provides a comprehensive overview of how foundation models are reshaping physical-layer processing and wireless resource management across three progressive paradigms. First, we examine the adaptation of off-the-shelf pre-trained foundation models to various wireless tasks. Second, we explore wireless-native foundation models, built from scratch on wireless data to bridge cross-domain modality gaps and capture universal wireless-domain physical characteristics. Third, we highlight agentic foundation models, which elevate static data processing into autonomous, reasoning-driven network orchestration. Furthermore, we discuss the impact of applying foundation models to emerging 6G frontiers, including integrated sensing and communications (ISAC), new multiple-input multiple-output (MIMO) architectures, semantic communications, and system-level network autonomy. Finally, we identify critical open challenges and opportunities, charting a promising path toward fully intelligent and adaptive wireless networks.
Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks
Duzce University（杜兹切大学）；University of Luxembourg（卢森堡大学）；Carleton University（卡尔顿大学）；United States Army Research Laboratory（美国陆军研究实验室）；University of Hertfordshire（赫特福德大学）
Wireless Physical Neural Networks (WPNNs): Opportunities and Challenges
Imperial College London（帝国理工学院）；Bar-Ilan University（巴伊兰大学）；TOBB University of Economics and Technology（TOBB经济与科技大学）；Université Paris-Saclay（巴黎萨克雷大学）；King’s College London（伦敦国王学院）
Edge-Native Embodied Intelligence for Action-Aware Wireless Edge Networks
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Queen’s University Belfast（贝尔法斯特女王大学）；Sungkyunkwan University（成均馆大学）
