Keynotes
6D Movable Antenna Empowered Wireless Network: The Road to 6G
Dr. Rui Zhang (Fellow of IEEE, Fellow of the Academy of Engineering Singapore) received the B.Eng. (first-class Hons.) and M.Eng. degrees from National University of Singapore and the Ph.D. degree from Stanford University, all in electrical engineering.
He is now a Provost’s Chair Professor with the Department of Electrical and Computer Engineering, National University of Singapore. He is also an Adjunct Professor with the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China.
His current research interests include intelligent surfaces, reconfigurable antennas, radio mapping, non-terrestrial communications, wireless power transfer, AI and optimization methods. He has published over 600 papers, which have been cited more than 110,000 times with the h-index over 150 (Google Scholar). He has been listed as a Highly Cited Researcher by Thomson Reuters / Clarivate Analytics since 2015.
He was the recipient of the IEEE Communications Society Asia-Pacific Region Best Young Researcher Award in 2011, the Young Researcher Award of National University of Singapore in 2015, the Recognition Award of WTC, SPCC and TCCN Technical Committees of the IEEE Communications Society in 2020, 2021 and 2023, respectively.
He received 18 IEEE Best Journal Paper Awards, including the IEEE Marconi Prize Paper Award in Wireless Communications (twice), the IEEE Communications Society Heinrich Hertz Prize Paper Award (thrice), the IEEE Communications Society Stephen O. Rice Prize, the IEEE Signal Processing Society Best Paper Award, etc.
He has served as an Editor for several IEEE journals, including TWC, TCOM, JSAC, TSP, etc., and as TPC co-chair or organizing committee member for over 30 international conferences. He served as an IEEE Distinguished Lecturer of IEEE Communications Society and IEEE Signal Processing Society.
He now serves as an Editorial Board Member of npj Wireless Technology, and the Chair of the IEEE Communications Society Wireless Communications Technical Committee (WTC) Award Committee.
Abstract
Six-dimensional movable antenna (6DMA) has been recently proposed as an innovative and transformative technology for future wireless networks. 6DMA offers unprecedented flexibility and reconfigurability in antenna 3D position and 3D rotation/orientation, significantly improving antenna agility and adaptability and enhancing wireless communication/sensing performance over conventional fixed-position antennas.
In this talk, we provide a comprehensive overview of 6DMA, including its historical development, practical architectures and implementation methods, as well as promising applications in 6G wireless networks.
We then introduce 6DMA fundamentals including its signal and channel models, practical antenna movement constraints, and address the main design challenges including antenna 3D position-rotation optimization and channel acquisition.
Various new architectures of 6DMA such as partially-movable 6DMA, polarized 6DMA, cell-free 6DMA, and passive 6DMA are also highlighted. Moreover, we present recent prototyping and experiment results to validate 6DMA performance under realistic channel settings.
Finally, we shed light on the research directions worthy of investigation in future work to unleash the full potential of 6DMA for wireless networks.
Integrated Communication-Sensing-Computing-Intelligence for 6G Wireless Networks Core Technologies
Zhang Haijun received his PhD from the joint training of Beijing University of Posts
and Telecommunications and King’s College London. He completed his postdoctoral
research at the University of British Columbia, Canada. Currently, he is a professor in
the Department of Computer and Communication Engineering at the University of
Science and Technology Beijing. His research direction is communication and
information systems, especially in intelligent 6G, sky-ground networks, and artificial
intelligence for next-generation wireless architectures. He also serves as the dean of
the School of Intelligent Science and Technology, a director of the China Electronics
Society, a director/fellow of the China Communications Society, a vice chairman of
the Youth Work Committee of the China Communications Society, and the director of
the Beijing University of Science and Technology-Sony China Joint Laboratory. He is
currently the chairman of the IEEE Green Communications and Computing Technical
Committee and an editorial board member of the IEEE Transactions on Network
Science and Engineering.
Professor Zhang is also a recipient of the National Outstanding Youth Fund, the IEEE
Communications Society Best Young Author Paper Award in 2017, the International
Union of Radio Science Young Scientist Award, the China Communications Society
Youth Science and Technology Award in 2018, the IEEE Communications Society
Asia-Pacific Most Outstanding Young Researcher Award in 2019, the China
Communications Society Natural Science First Prize in 2021, and the Beijing Youth
Science and Technology Award of the 2024 Mao Yisheng Science and Technology
Award. He is an IEEE Fellow and an AALA Fellow.
Abstract
Future 6G wireless networks will possess the capability to perceive everything, requiring deep integration of key technologies such as communication, sensing, computing, and intelligence. This integration will form a 6G wireless network empowered by integrated communication, sensing, computing, and intelligence capabilities, ensuring flexible and stable transmission of massive, heterogeneous information data. This report will begin with the key technologies of integrated communication-sensing-computing-intelligence in 6G wireless networks, elaborating on how to achieve stable communication guarantees in network architecture, interference management, and multi-domain resource allocation. It will further facilitate effective organization and synergy among communication, sensing, computing, and intelligence resources in future 6G wireless networks, promoting deep convergence and mutual enhancement between future 6G wireless networks and the integrated communication-sensing-computing-intelligence paradigm.
FDA Improves Large Language Model Performance in Traffic Prediction
Bio: Shiwen Mao is a Professor and Earle C. Williams Eminent Scholar, and Director of the Wireless Engineering Research and Education Center at Auburn University.
Dr. Mao’s research interest includes wireless networks, multimedia communications, and smart grid. He is the Editor-in-Chief of IEEE Transactions on Cognitive Communications and Networking, the Technical Committee Board Director, and a Board of Governors Member-at-Large of IEEE Communications Society.
He received the SEC (Southeastern Conference) 2023 Faculty Achievement Award for Auburn, the Auburn University Creative Research & Scholarship Award in 2018, the NSF CAREER Award in 2010, and several service awards from the IEEE.
He is a co-recipient of several journal and conference best paper/demo awards from the IEEE. He is a Fellow of the IEEE.
Abstract
In communication network management, prediction of mobile network traffic is essential to ensure efficient system operation. Although significant progress has been made in the application of neural networks to traffic prediction tasks, traditional models still face considerable challenges when handling high-dimensional and highly time-dependent data.
To address these issues, this talk proposes a new prediction framework that leverages large language models (LLMs), by constructing efficient prompts to enhance the ability of large language models (LLMs) in traffic prediction and improve their understanding of complex traffic patterns.
Specifically, we introduce functional data analysis (FDA), a technique that offers superior capabilities compared to traditional methods in processing continuous and high-dimensional data structures, to preprocess traffic data and extract key features.
Extensive experiments conducted on multiple LLMs using a real-world dataset validate the effectiveness and scalability of the proposed method. The experimental results demonstrate that the framework achieves significant improvements in predictive performance, providing a promising and efficient solution for traffic data analysis in future communication networks.
