Fellow of IEEE
Prof. Kaibin Huang, The University of Hong Kong (HKU), Hong Kong, China

Kaibin Huang 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 Wong Wilson Wong Professor 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 RGC Research Fellow (2021 Class). He has served on the editorial boards of five major journals and co-edited ten journal special issues, all in the area of wireless communications. He has been active in organizing international conferences such as the 2014, 2017, and 2023 editions of IEEE Globecom, a flagship conference in communication. He has been named as a Highly Cited Researcher by Clarivate in the last six years (2019-2024) and an AI 2000 Most Influential Scholar (Top 30 in Internet of Things) in 2023-2024. He was an IEEE Distinguished Lecturer. He is a Fellow of the IEEE and the US National Academy of Inventors.

Speech Title: Pushing AI to the 6G Edge

Abstract: 6G will feature edge intelligence referring to ubiquitous deployment of AI algorithms at the network edge. To attain unprecedented end-to-end (E2E) performance, researchers embrace the new design approach of integrated communication-and-computing (iCC). While 5G allows coarse message exchange (e.g., performance requirements) between application and physical layers, the new iCC approach in 6G features joint design and control of AI and communication algorithms under E2E objectives. In this talk, I will provide an overview of the design approach and advancements in 6G edge intelligence. Many topics will be covered including ultra-low-latency edge AI, over-the-air computing, in-memory baseband processing, distributed sensing, in-network inference, and AI model downloading.

Fellow of IEEE
Prof. Rui Zhang, The Chinese University of Hong Kong, Shenzhen and National University of Singapore

Dr. Rui Zhang 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 Principal’s Diligence Chair Professor in School of Science and Engineering and Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen. He is also a Professor with the Department of Electrical and Computer Engineering, National University of Singapore. His current research interests include wireless power transfer, UAV/satellite communications, intelligent reflecting surface (IRS) and reconfigurable MIMO systems. He has published over 500 papers, which have been cited more than 100,000 times with the h-index over 130 (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 is a Fellow of IEEE and the Academy of Engineering Singapore.  

Speech Title: Movable Antenna Aided Wireless Networks: Opportunities and Challenges

Abstract: Movable antenna (MA) has been recently recognized as a promising technology for enhancing wireless communication/sensing performance by exploiting wireless channel spatial variation via antenna movement at the transceiver. In this talk, we provide a comprehensive overview of MAs, including their historical development, practical architectures and implementation methods, contemporary applications in wireless networks, as well as mathematical models, design issues and promising approaches to solve them. Various performance advantages of MAs over conventional fixed-position antennas (FPAs) are demonstrated, in terms of spatial diversity/multiplexing, interference mitigation, and flexible beamforming. Furthermore, a general six-dimensional MA (6DMA) system is introduced, which consists of distributed antenna arrays that can be independently adjusted in terms of 3D position and 3D rotation to achieve the greatest flexibility in antenna movement. It is shown that by jointly designing the positions and rotations of all 6DMA arrays equipped at the base station (BS) based on the users’ statistical channel distribution, the wireless network capacity can be significantly improved over the existing BS with FPAs (e.g., sector antennas). Finally, we shed light on the research directions worthy of investigation in future work to unleash the full potential of MAs for wireless networks. 

Fellow of IEEE
Prof. Yong Zeng, Southeast University, China

Yong Zeng, IEEE Fellow, young chief professor of Southeast University and Purple Mountain Laboratory, national youth high-level talent, Jiangsu province distinguished young researcher, Clarivate Analytics Highly Cited Researcher for 6 consecutive years (2019-2024), AI2000 Most Influential Scholars in the field of Internet of Things for 4 consecutive years (2021-2024), Stanford "Top 2% of Scientists in the World - Lifetime Influence". Prof. Zeng is the recipient of Australia Research Council (ARC) Discovery Early Career Researcher Award (DECRA), IEEE Communications Society Asia-Pacific Outstanding Young Researcher Award, and won 8 international and domestic best paper awards including IEEE Marconi Award (2020 and 2024), Heinrich Hertz Award (2017 and 2020), etc. Prof. Zeng proposed the concept of channel knowledge map (CKM), and his works have been cited by more than 29,000 times. He serves on the editorial board of SCI journals such as IEEE Transactions on Communications, IEEE Transactions on Mobile Computing, and IEEE Communications Letters, and leading guest editor of journals including IEEE ComMag, Wireless ComMag, China Communications, and Science China Information Sciences. Prof. Zeng was elevated to IEEE Fellow“for contributions to unmanned aerial vehicle communications and wireless power transfer”.

Speech Title: Generative AI based Channel Knowledge Map Construction and Utilization

Abstract: Existing wireless communication and sensing systems are mainly based on the traditional “environment-unaware” paradigm, which fails to fully exploit the prior information of the local wireless environment, resulting in inefficient environment sensing and channel acquisition. This makes it difficult to meet the future needs with the developing trends such as larger channel dimensions, higher node densities, and more cost-effective hardware. On the other hand, the recently proposed concept of channel knowledge map (CKM) aims to build channel knowledge foundations that learn the intrinsic characteristics of the local wireless environment by fusing massive historical data of all terminals in the area, thereby enables the direct acquisition of environmental priors in advance based on (virtual) terminal location information. This enables the paradigm shift from the traditional environment-unaware to the future environment-aware communication and sensing, offering new ideas for efficient environment sensing and channel acquisition. This talk will introduce the latest research progress in the construction and application of CKM. By discussing the basic principles of CKM, typical cases of communication and sensing based on CKM, the theories and methods of CKM construction based on generative AI, as well as preliminary experimental verification, we will try to answer the five fundamental questions about CKM (2W+3H): What is CKM, why needs CKM, how to build and utilize CKM, and how to build prototypes?

Fellow of IEEE
Prof. Sanghoon Lee, Yonsei University, South Korea

Sanghoon Lee received his Ph.D. in EE from the University of Texas at Austin in 2000. He worked at Korea Telecom (1991–1996) and Lucent Technologies (1999–2002). He has been serving as the Deputy Editor-in-Chief/Associate Editor of the IEEE Transactions on Multimedia (2024–/2022–) and a Member of the Senior Editorial Board of the IEEE Signal Processing Magazine (2022–). He was an Associate/Guest Editor of the IEEE Transactions on Image Processing (2010–2014, 2013), and an Associate/Senior Area Editor of the IEEE Signal Processing Letters (2014–2022). He also served as the Chair of the Asia-Pacific Signal and Information Processing Association (APSIPA) IVM Technical Committee (2018–2019), an APSIPA BoG member (2020, 2022–2024), the Editor-in-Chief of APSIPA Newsletters (2022–2023), the Chair of the IEEE P3333.1 Quality Assessment Working Group (2011–2024), and the President of the Korean Society for Simulation Surgery (2023–2024). Including service as the General Chair of the 2013 IEEE IVMSP Workshop, he has served as an organizing committee member for major conferences such as IEEE ICASSP, IEEE ICME, IEEE ICIP, and APSIPA ASC. He has also been active as a keynote speaker, invited speaker, and panelist at numerous academic conferences. He has received the Academic (2015), Contribution (2021), and Best Engineering Professor (2023) Awards from Yonsei University, the Chairman’s Award from the Presidential Council on Intellectual Property (2021), the Outstanding Area Chair Award at IEEE ICME 2020, the Best Student Paper Award at QoMEX 2018, the IEEE Transactions on Multimedia Excellent Editor Award (2023, 2024), and the Best Demo Paper Award at ACM Multimedia 2024.

Speech Title: Redefining Reality: Multi-Camera Systems for Photorealistic Human Avatars

Abstract: With the advent of generative AI, the future of Metaverse technologies is evolving toward breathtaking movie graphics, immersive video games, and advanced 4D content. To drive the advancement of such 4D generative AI technologies, it is essential to approach the core technology of avatar creation. Since 2019, we have embarked on a journey to achieve the perfect 4D avatar and have been working to integrate this into generative AI-based content. Our current state-of-the-art dome-type multi-camera system enables precise, high-resolution avatar capture in a multi-illuminant environment using deep learning techniques. In this keynote, I will introduce the technologies developed in our lab and discuss the future direction we should take through a comparative analysis with technologies from Meta, Google, Microsoft, and Netflix. In particular, I will present key technologies such as Multi-View Object Registration, 4D Gaussian Splatting, and 4D Light Control Diffusion. I will also explore how these technologies can be integrated into future content applications.