Keynote Speakers

Xuemin (Sherman) Shen
University of Waterloo, Canada

Xuemin (Sherman) Shen is an University Professor at the University of Waterloo, Canada. His research interests include network resource management, wireless network security, AI for networks, and future communication systems. Professor Shen is the Technical Program Committee Chair for IEEE Globecom'24, Globecom'16, IEEE Infocom14, and IEEE VTC’10 Fall. He was the Editor-in-Chief of the IEEE Internet of Things Journal, IEEE Network, and IET Communications. Professor Shen served as the 2022-2023 President of IEEE Communications Society. He is a Fellow of the IEEE, Royal Society of Canada, Canadian Academy of Engineering, and Engineering Institute of Canada, a Foreign Member of Chinese Academy of Engineering, and an International Fellow of the Engineering Academy of Japan.

 

Title: Flexible Coupler Antenna for Wireless Networks: Opportunities and Challenges
Abstract:
The flexible coupler antenna (FCA) is a potentially innovative technology for 6G wireless networks. It leverages spatial variations in wireless channels by adjusting the positions or orientations of low-cost passive couplers around fixed active antennas, thereby enhancing network performance. In this presentation, we first introduce the signal and channel models for FCA, considering practical hardware constraints. We then highlight its performance advantages as compared with fixed-position active antennas and other reconfigurable MIMO technologies. Finally, we discuss the key challenges in FCA-based wireless networks and outline promising research directions to fully realize the potential of FCA for 6G systems.

Shuguang Cui
The Chinese University of Hong Kong, Shenzhen, China

Shuguang Cui received his Ph.D in Electrical Engineering from Stanford University, California, USA, in 2005. Afterwards, he has been working as assistant, associate, full, Chair Professor in Electrical and Computer Engineering at the Univ. of Arizona, Texas A&M University, UC Davis, and CUHK at Shenzhen respectively. He was selected as the Thomson Reuters Highly Cited Researcher and listed in the Worlds’ Most Influential Scientific Minds by ScienceWatch in 2014. He was the recipient of the IEEE Signal Processing Society 2012 Best Paper Award. He was elected as an IEEE Fellow in 2013, an IEEE ComSoc Distinguished Lecturer in 2014, and IEEE VT Society Distinguished Lecturer in 2019. In 2023, he won the IEEE Marconi Best Paper Award, got elected as a Fellow of both Canadian Academy of Engineering and the Royal Society of Canada, and started to serve as the Editor-in-Chief for IEEE Transactions on Mobile Computing. In 2024, he won the National 2nd Prize in Nature Science and 1st Science and Technology Prize from CIC of China. In 2026, his CVPR paper got a full mark, the CVPR Compute Transparency Champion Award, and the honor of Best Paper Finalist (0.37%).

 

Title: Generative AI Redefines Intelligence in Wireless Networking

Abstract:

Currently, the most intelligent computation model is AI, more specifically Generative AI (GAI). In this talk, we discuss how to redefines the network intelligence by applying various GAI-driven approaches, across three layers: 1) How to better understand the wireless environment via AGI; 2) How to better control a wireless network via AGI; and 3) How to better manipulate user data via AGI.

 

Ying-Chang Liang
University of Electronic Science and Technology of China, China

Ying-Chang Liang [IEEE Fellow] is currently a Professor at the University of Electronic Science and Technology of China. He was a Professor with the University of Sydney, Australia, and the Principal Scientist of the Institute for Infocomm Research (I2R), A*STAR, Singapore. His research interests include 5G/6G networks, cognitive radio, symbiotic radio, IoT, and AI. He is an elected Member at Large of the IEEE Communications Society’s Board-of-Governors (2025-2027). He served the Founding Editor-in-Chief of the IEEE JSAC - Cognitive Radio Series (2011-2014), Chair of the Technical Committee on Cognitive Networks (2015-2016), and Editor-in-Chief of IEEE Transactions on Cognitive Communications and Networking (2019-2022). In addition, he served as TPC Chair and Executive Co-Chair of IEEE Globecom’17, TPC Co-Chair of IEEE Globecom’24, and General Chair of IEEE VTC2025-Fall. He received several research and publication awards, including the IEEE VTS Stuart Meyer Memorial Award (2025), IEEE ComSoc Award for Advances in Communications (2022), IEEE ComSoc Stephen O. Rice Prize (2021), and IEEE ComSoc TCCN Recognition Award (2018). He is a Fellow of IEEE, and a Clarivate Analytics’ Highly Cited Researcher since 2014.

 

Title: Integrated Sensing and Backscatter Communications for Low-Altitude Economy

Abstract:

In low altitude economy, communications, sensing and target identification are core requirements for the low altitude information network design. In this talk, backscatter communication is proposed for target identification, and multi-functional spectrum sharing is used to support integrated communications, sensing and identification (ISCI) with enhanced resource utilization efficiency.

 

Sumei Sun
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore

Dr Sumei Sun is professor, Director of Information and Communication Technologies Cluster, Head of Future Communications Translation Laboratory at Singapore Institute of Technology (SIT), Singapore’s University for Industry. She holds a joint appointment with A*STAR’s newly formed Institute of Advanced Intelligence and Computing as Senior Advisor. Before that, she was Executive Director of A*STAR Institute for Infocomm Research, and Coordinating Executive Director for Transport and Connectivity. She also holds an adjunct professor appointment with the National University of Singapore.
With strong passion in industry-relevant research and technology creation, Sumei has authored and co-authored more than 400 technical papers. She is the inventor/co-inventor of over thirty patented technologies, with most of them licensed to industry. She’s a Fellow of the Academy of Engineering Singapore (SAEng), Fellow of the IEEE, AI Industry Alliance (AIIA), and Asia-Pacific AI Association (AAIA). Sumei’s current research interests include next-generation communications, sensing-communications-computing-control integrative design, applied AI, and next-generation transportation systems. She’s recipient of the IEEE ComSoc Donald W. McLellan Meritorious Service Award 2024, IEEE ComSoc Inaugural Asia Pacific Women in Communications Engineering Outstanding Achievement Award (2024), 2023 IEEE VTS Women’s Distinguished Career Award, and Singapore National Day 2022 Public Administration Medal (Bronze).

 

Title: Pervasive Sensing and Connectivity for Real-Time and Embodied Intelligence

Abstract:

Digitalization, decarbonization, intelligence, automation, and autonomy are driving the next frontier of industrial transformation. Realizing this vision requires high-fidelity real-time sensing, pervasive connectivity, network-native AI, and connected and embodied intelligence seamlessly spanning the physical and digital worlds. AI-native integrated 5G and emerging 6G terrestrial and non-terrestrial networks (TN–NTN), along with other communication technologies will provide the intelligent connectivity fabric, enabling distributed sensing, collaborative intelligence, autonomous decision-making, and resilient edge–cloud orchestration for next-generation cyber-physical systems.
Using maritime and aviation sectors as examples, this presentation will illustrate how AI and embodied AI is transforming traffic management, operational efficiency, safety, and decarbonization. It will highlight the critical role of high-fidelity spatio-temporal data in enabling real-time AI and Hybrid Twins, and present a roadmap for AI-native 6G, network-native AI, and intelligent sensing as foundational technologies for connected real-time and embodied intelligence. The talk will conclude with emerging research opportunities and future directions toward pervasive sensing, and ubiquitous connectivity for connected and embodied intelligence.

 

Chengwen Xing
Beijing Institute of Technology, China

Chengwen Xing is a Distinguished Professor at Beijing Institute of Technology, and a recipient of the National Science Fund for Distinguished Young Scholars of China. He received the B.Eng. degree from Xidian University, Xi'an, China, in 2005 and the Ph.D. degree from the University of Hong Kong, Hong Kong, China, in 2010. After completing his doctorate, he has been with the School of Information and Electronics, Beijing Institute of Technology, Beijing, China. His research focuses on array signal processing for space-air communications, and has published more than 200 papers in IEEE Transactions, and has won 2019 Second Prize of National Technology Invention, 2025 First Prize of Natural Science of China Institute of Communications, 2020 First Prize of Civil Aviation Science and Technology of China Air Transport Association.

 

Title: Energy-Efficient Design for Movable Antenna-Aided MU-MIMO Systems

Abstract:

Movable antennas provide a new degree of freedom for wireless communications by exploiting spatial channel variations, but also introduce challenges such as strong coupling between antenna positions and transmission strategies, high-dimensional optimization variables, and severe non-convexity. To address these issues, this report investigates the energy-efficiency optimization problem in movable antenna-aided multi-user MIMO systems and proposes a general framework that accommodates various power constraints and practical antenna position constraints. By combining WMMSE and successive convex approximation techniques, the proposed approach effectively handles the non-convexity caused by position coupling. In addition, two low-complexity beamforming design strategies are developed to reduce the computational burden of conventional fractional programming methods.

 

Yingjun Zhang
The Chinese University of Hong Kong, China

Angela Yingjun Zhang received her Ph.D. degree from the Department of Electrical and Electronic Engineering, The Hong Kong University of Science and Technology. She joined the Department of Information Engineering, The Chinese University of Hong Kong in 2005, where she is now a professor.
Prof. Zhang is now the Steering Committee Chair of IEEE Wireless Communication Letters, Director of IEEE ComSoc Asia Pacific Region Board, and member of IEEE VTS Fellow Evaluation Standing Committee. Previously, she served as Editor-in-Chief of IEEE Open Journal of the Communications Society, Chair of the Executive Editor Committee of IEEE Transactions on Wireless Communications, and member of IEEE ComSoc Fellow Evaluation Standing Committee. Prof. Zhang has served on the Organizing Committees of many top conferences, such as IEEE GLOBECOM, ICC, VTC, SmartgridComm, and was the Chair of IEEE ComSoc Technical Committee of Smart Grid Communications.
Prof. Zhang is a co-recipient of 2021 and 2014 IEEE ComSoc Asia Pacific Outstanding Paper Awards, 2013 IEEE SmartgridComm Best Paper Award, and 2011 IEEE Marconi Prize Paper Award on Wireless Communications. As the only winner from engineering science, Prof. Zhang won the Hong Kong Young Scientist Award 2006, conferred by the Hong Kong Institute of Science.

 

Title: Score-Empowered 6G Integrated Sensing and Communications: When Wireless Meets Generative Modeling

Abstract:

Integrated sensing and communications (ISAC) is a key enabling technology for 6G, but its performance depends critically on prior knowledge of targets, environments, and propagation conditions. Conventional approaches often rely on simple parametric priors, which may fail to capture complex and non-Gaussian wireless statistics. In this talk, we introduce a score-empowered ISAC framework that uses learned score functions to represent complex priors while retaining physics-based sensing and communication models. A score function is the gradient of a distribution’s log-density, learnable from data to capture statistical structures such as non-Gaussianity and multimodality. We first discuss how score functions can characterize sensing performance and enable prior-aware physical-layer design and optimization. We then show how learned score priors can be embedded into Bayesian inference algorithms to improve efficiency and accuracy. Overall, this framework provides a unified interface between data-driven learning and model-based wireless design, and highlight promising directions toward intelligent and adaptive 6G sensing and communication systems.