This book systematically examines the integration of language models with wireless networks from both architectural and algorithmic perspectives. It begins with the evolution of language models and wireless network requirements, followed by core concepts such as model scaling, training, inference, and the resource constraints shaping their deployment. Building on this foundation, the book investigates LLMs in cloud environments and SLMs for edge computing, focusing on compression, distillation, and efficiency under constrained conditions. The central part of the book is structured around two complementary directions. The first, network-aided collaborative language models, explores how cloud and edge language models can jointly support distributed inference through model partitioning, collaborative training, and adaptive coordination, considering synchronization and communication constraints in wireless networks. The second, language model-aided network optimization, focuses on using language models as decision-making agents to improve network performance, covering protocol optimization, expert routing, and cross-layer integration. These technical developments are grounded through detailed application scenarios and case studies, analyzing trade-offs between accuracy, latency, and resource consumption. The book concludes with forward-looking discussions on architecture, deployment strategies, and research challenges, serving as a comprehensive reference for researchers and practitioners at the intersection of wireless networks and artificial intelligence.
Introduction.- Large Language Models and Wireless Networks.- Large
Language Models in the Cloud.- Small Language Models at the Edge.-
Network-aided Collaborative Language Models.- Language Model-aided Network
Optimization.- Applications and Case Studies.- Future Directions and Emerging
Trends.- Conclusion.
Hongyang Du is an assistant professor at the Department of Electrical and Electronic Engineering, The University of Hong Kong, where he directs the Network Intelligence and Computing Ecosystem (NICE) Laboratory. He received the B.Eng. degree from the Beijing Jiaotong University, China, and the Ph.D. degree from the Nanyang Technological University, Singapore. He serves as the Editor of IEEE Communications Surveys & Tutorials, IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, and IEEE Open Journal of the Communications Society. He is the recipient of the IEEE ComSoc Young Professional Award for Best Early Career Researcher in 2024, the IEEE Signal Processing Society Scholarship from the IEEE Signal Processing Society in 2023, and IEEE Daniel E. Noble Fellowship Award from the IEEE Vehicular Technology Society in 2022. His research interests include edge intelligence, generative AI, and communication networks.
Xianhao Chen received the B.Eng. degree in electronic information from Southwest Jiaotong University, China, in 2017, and the Ph.D. degree in electrical and computer engineering from the University of Florida in 2022. He is currently an Assistant Professor at the Department of Electrical and Electronic Engineering, The University of Hong Kong, where he directs the Wireless Information and Intelligence (WILL) Laboratory. His research interests include wireless networking, edge intelligence, and machine learning.
Yuanwei Liu is a tenured full Professor in Department of Electrical and Electronic Engineering (EEE) at The University of Hong Kong (HKU), and also a visiting Professor in Queen Mary University of London. He is IEEE Fellow, AAIA Fellow, AIIA Fellow, web of Science Highly Cited Researcher (2021 to present), young member of the Hong Kong Academy of Engineering. His research interests include pinching antenna systems, next generation multiple access, integrated sensing and communications, reconfigurable intelligent surface, near-field communications and mobile edge generation. He is listed as one of 35 Innovators Under 35 China in 2022 by MIT Technology Review. He serves as an IEEE Communication Society Distinguished Lecturer, an IEEE Vehicular Technology Society Distinguished Lecturer, chair of IEEE Signal Processing and Computing for Communications (SPCC) Technical Committee, the academic Chair for the Next Generation Multiple Access Emerging Technology Initiative. He received IEEE ComSoc Outstanding Young Researcher Award for EMEA in 2020. He received the 2020 IEEE SPCC Technical Committee Early Achievement Award, IEEE Communication Theory Technical Committee (CTTC) 2021 Early Achievement Award. He received IEEE ComSoc Outstanding Nominee for Best Young Professionals Award in 2021. He received four IEEE best paper awards. He serves Co-Editor-in-Chief of IEEE ComSoc Technical Newsletter, Area Editor of IEEE TCOM/CL, Editor of IEEE COMST/TWC/TCCN /TVT/TNSE, (leading) guest editor of Proceedings of IEEE/IEEE JSAC/JSTSP etc., and the rapporteur of ETSI Industry Specification Group on RIS Industry Specification Group Work Item 6.
Kaibin Huang (Fellow, IEEE) received the B.Eng. and M.Eng. degrees from the National University of Singapore and the Ph.D. degree from The University of Texas at Austin, all in electrical engineering. He is the Philip K H Wong Wilson K L Wong Professor in Electrical Engineering and the Department Head at the Dept. of Electrical and Electronic Engineering, The University of Hong Kong (HKU), Hong Kong. His work was recognized with seven Best Paper awards from the IEEE Communication Society. He is a member of the Engineering Panel of Hong Kong Research Grants Council (RGC) and a Croucher Senior Research Fellow (2026 Class).
