Chair Prof. Liuqing Yang

IEEE Fellow

The Hong Kong University of Science and Technology (Guangzhou), China

Bio：Prof. Liuqing Yang received the Ph.D. degree from the University of Minnesota, Minneapolis, MN, USA, in 2004. She is a Fellow of IEEE and AAIA. Prof. Yang has been a faculty member with University of Florida, Colorado State University, and University of Minnesota, and is currently a Chair Professor with the Hong Kong University of Science and Technology (Guangzhou), where she serves as the Acting Director of the Low-Altitude Systems and Economy Research Institute (LASERi), the Head of the Intelligent Transportation (INTR) Thrust, and the Director of the Seamless Connectivity and Connected Intelligence (SC2I) Center. Her research interests include communications, sensing, and networked intelligence, subjects on which she has published more than 400 journal and conference papers, four book chapters, and five books. She is a recipient of the ONR Young Investigator Program (YIP) Award in 2007, the NSF Faculty Early Career Development (CAREER) Award in 2009, and the Best Paper Award at IEEE ICUWB 2006, ICCC 2013, ITSC 2014, GLOBECOM 2014, ICC 2016, WCSP 2016, GLOBECOM 2018, ICCS 2018, and ICC 2019. Prof. Yang is an Executive Editorial Committee (EEC) Member of the IEEE Transactions on Wireless Communications and a Senior Editor of the IEEE Transactions on Intelligent Vehicles. She has also served as the Editor-in-Chief of IET Communications, on the editorial board for an array of elite journals including IEEE Transactions on Signal Processing, IEEE Transactions on Communications, and the IEEE Transactions on Intelligent Transportation Systems, in various roles of IEEE ComSoc and IEEE ITSS, as well as in leadership roles for many conferences.

Title: AI for Channel Prediction: Physics Informed or Large Model Dominated?

Abstract: In complex wireless scenarios, predicting future CSI is crucial for effective decision-making. Traditional channel prediction relies on a purely physical model, which is limited in accuracy due to the complexity of the underlying propagation environment. The emergence of AI has brought about a revolution in the wireless field, but concerns remain regarding the interpretability and generalization of AI models. In this presentation, we will explore two promising approaches to AI-based channel prediction. The first approach involves enhancing small-scale AI networks with physics-based knowledge. A case study will delve into the integration of multi-modality sensing with communications to develop a pilot-free channel predictor for vehicular networks. The second approach focuses on utilizing large-scale AI models, specifically Large Language Models (LLMs), to address wireless tasks. A case study will showcase how a Frequency Division Duplex (FDD) system can utilize an adapted LLM to convert channel prediction into a time-series task.

Finally, we will compare and contrast the two approaches for utilizing AI in wireless applications, and discuss potential research directions in this evolving field.

Prof. Dusit (Tao) Niyato

IEEE Fellow, IET Fellow

Nanyang Technological University (NTU), Singapore

Bio：Dusit Niyato is currently a President's Chair Professor in the College of Computing & Data Science (CCDS), Nanyang Technological University, Singapore. He received B.E. from King Mongkuk’s Institute of Technology Ladkrabang (KMITL), Thailand in 1999 and Ph.D. in Electrical and Computer Engineering from the University of Manitoba, Canada in 2008. Dusit's research interests are in the areas of distributed collaborative machine learning, Internet of Things (IoT), edge intelligent generative AI and AI-generated content (AIGC), mobile and distributed computing, and wireless networks. Dusit won the IEEE Vehicular Technology Society Stuart Meyer Memorial Award, and the IEEE Communications Society (ComSoc) Best Survey Paper Award, IEEE Asia-Pacific Board (APB) Outstanding Paper Award. Currently, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials (impact factor of 34.4 for 2023), an area editor of IEEE Transactions on Vehicular Technology, editor of IEEE Transactions on Wireless Communications, associate editor of IEEE Internet of Things Journal, IEEE Transactions on Mobile Computing, IEEE Wireless Communications, IEEE Network, IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Cognitive Communications and Networking (TCCN), IEEE Data Descriptions, IEEE Transactions on Services Computing, IEEE Communications Magazine, and ACM Computing Surveys. He was also a guest editor of IEEE Journal on Selected Areas on Communications. He is the Members-at-Large to the Board of Governors of IEEE Communications Society for 2024-2026. He was named the 2017-2023 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET.

Title: Large Language Models (LLMs) with Retrieval-Augmented Generation for Next Generation Networking

Abstract: With the advance of artificial intelligence (AI), the emergence of Google Gemini and OpenAI Q* marks the direction towards artificial general intelligence (AGI). To implement AGI, the concept of interactive AI (IAI) with large language models (LLMs) has been introduced, which can interactively understand and respond not only to human user input but also to dynamic system and network conditions. In this presentation, we explore an integration and enhancement of LLMs in networking. We first comprehensively review recent developments and future perspectives of AI and then introduce the technology and components of IAI and LLMs. We then explore the integration of IAI and LLMs into the next-generation networks, focusing on how implicit and explicit interactions can enhance network functionality, improve user experience, and promote efficient network management. Subsequently, we propose an IAI-enabled network management and optimization framework, which consists of environment, perception, action, and brain units. We also design the pluggable large language model (LLM) module and retrieval augmented generation (RAG) module to build the knowledge base and contextual memory for decision-making in the brain unit. We demonstrate the effectiveness of the framework through case studies. Finally, we discuss potential research directions for IAI-based networks.

Prof. Guangjie Han

IEEE Fellow

Hohai University, China

Bio: Guangjie Han is a professor, currently serving as the Dean of the School of IoT Engineering at Hohai University. He is an IEEE Fellow, IET/IEE Fellow, and AAIA Fellow. His main research interests include smart oceans, industrial IoT, artificial intelligence, networks, and security. In recent years, he has published more than 350 high-level SCI journal papers, including over 130 papers in the IEEE/ACM Trans. series, in international journals such as IEEE JSAC, IEEE TMC, IEEE TPDS, and IEEE TCC. His publications have been cited over 17200 times on Google Scholar, with an H-index of 68. He has authored three monographs and translated one book. He has led more than 30 provincial and ministerial-level research projects, including national key R&D programs and national natural science foundation key projects. He has been granted 130 national invention patents and 6 PCT international authorized patents. He has received numerous awards, including the second prize of the China Business Federation Science and Technology Award, the third prize of the Jiangsu Provincial Science and Technology Award, the second prize of the Liaoning Provincial Science and Technology Progress Award, and the Best Paper Award of the IEEE Systems Journal in 2020. For five consecutive years (2019-2023), he has been listed as one of the top 2% of scientists globally, as well as for the Chinese Highly Cited Researchers list for four consecutive years (2020-2023). Currently, he serves as an associate editor for more than ten international journals, including IEEE TII, IEEE TVT, IEEE TCCN, and IEEE Systems. He has been awarded the "333 High-level Talents in Jiangsu Province" (second level), the "Outstanding Contribution Young and Middle-aged Experts in Jiangsu Province," the "Minjiang Scholar Lecture Professor," and the "May 1st Labor Medal" of Changzhou City.

Title: Multi-Dimensional Dynamic Trust Management Mechanism in Underwater Acoustic Sensor Networks

Abstract: The underwater acoustic sensor network (UASN) is the core module to realize the "smart ocean". At present, the UASN has not yet fully played its role in the complex water environment. The fundamental reason lies in the lack of effective methods to ensure network security and reliable data transmission. This report mainly introduces the team's research work on the trust management mechanism of UASNs. The main research contents include: 1) Intrusion detection algorithm based on energy consumption prediction model; 2) Multi-dimensional trust calculation algorithm based on fuzzy theory; 3) Trust evaluation algorithm based on cloud theory; 4) Trust cloud migration mechanism based on AUV; 5) Trust update mechanism based on reinforcement learning; 6) Anomaly-resilient trust model based on isolation forest. The research results have important theoretical value and practical significance for exploring the security technology and application of UASNs.

Assoc. Prof. Jie Xu

The Chinese University of Hong Kong, Shenzhen, China

Bio: Jie Xu is currently an Associate Professor (Tenured) with the School of Science and Engineering, the Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen), and the Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong, Shenzhen, China. His research interests include wireless communications, wireless information and power transfer, UAV communications, edge computing and intelligence, and integrated sensing and communication (ISAC). He was a recipient of the 2017 IEEE Signal Processing Society Young Author Best Paper Award, the 2019 IEEE Communications Society Asia-Pacific Outstanding Young Researcher Award, and the 2019 Wireless Communications Technical Committee Outstanding Young Researcher Award. He is the Vice Chair of the IEEE Wireless Communications Technical Committee (WTC), and the Vice Co-chair of the IEEE Emerging Technology Initiative (ETI) on ISAC. He served or is serving as an Associate Editor-in-Chief of the IEEE Transactions on Mobile Computing, an Editor of the IEEE Transactions on Wireless Communications, IEEE Transactions on Communications, IEEE Wireless Communications Letters, and Journal of Communications and Information Networks, and a Guest Editor of the IEEE Wireless Communications, IEEE Journal on Selected Areas in Communications, IEEE Internet of Things Magazine, Science China Information Sciences, and China Communications. He is a Distinguished Lecturer of IEEE Communications Society.

Title: Integrated Sensing and Communications (ISAC): Fundamental Limits, Beamforming Design, and Network Cooperation

Abstract: Integrated Sensing and Communications (ISAC) has emerged as one of the key usage scenarios for 6G networks, offering a unified platform for simultaneous wireless communication and environmental sensing. This talk discusses the latest advancements in ISAC, focusing on the technical challenges, fundamental theoretic limits, practical beamforming design, and large-scale network cooperation. First, we explore the fundamental Cramér-Rao Bound (CRB)-Rate tradeoff limits for multi-antenna ISAC, shedding light on the inherent performance tradeoffs between sensing accuracy and communication efficiency. Next, we delve into the practical design of waveforms and beamforming strategies aimed at optimizing this balance. In addition, we further present the base station (BS) cooperation to enable large-scale networked ISAC, and exploit the application of Intelligent Reflecting Surfaces (IRS) to facilitate both sensing and communications.