Prof. Yang Yang, (IEEE Fellow), the Shanghai Center, Hong Kong University of Science and Technology, China

Professor Yang Yang is currently the Director of Shanghai Center, The Hong Kong University of Science and Technology (HKUST), China. He is also an adjunct professor with the Department of Broadband Communication at Peng Cheng Laboratory, and the Chief Scientist of IoT at Terminus Group, China. Yang's research interests include multi-tier computing networks, 5G/6G systems, AIoT technologies and applications, and advanced wireless testbeds. He has published more than 380 papers and filed more than 120 technical patents in these research areas. He is a fellow of the IEEE.

Speech Title: NASCA: Network Agentic Service Customization Architecture

Abstract: In wireless networks, the inherent conflict between limited computational resources and high-intensity computing workloads makes it extremely challenging to deploy complex tasks based on large AI models in edge devices. Traditional methods rely on aggregating data and transmitting it to cloud platforms for centralized processing; however, this approach is unsustainable as it strains long-distance backhaul transmission and energy-intensive data centers, while also failing to guarantee data security and personal privacy. To address these challenges, we propose the Network Agentic Service Customization Architecture (NASCA) for supporting personalized services. By leveraging the collaborative functions among distributed edge devices, we achieve fast, low-cost, and sustainable edge computing for large AI models. We analyze the engineering issues involved in the coordinated scheduling of multi-element resources—such as communication, sensing, computing, and storage—among edge devices, and verify the feasibility and service efficiency of different parallel processing schemes for large AI models.

Prof. Xiaojun Yuan, (IEEE Fellow), University of Electronic Science and Technology of China, China

Dr. Xiaojun Yuan is a Distinguished Professor at the National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China (UESTC), and an IEEE Fellow. He has long been dedicated to cutting-edge fundamental research in statistical signal processing, information theory, and machine learning. He has published over 300 papers (180+ IEEE journals) with over 10,000 Google Scholar citations. His honours include 3 Huawei Spark Awards, the IEEE Heinrich Hertz Best Paper Award, and the IEEE Jack Neubauer Best Paper Award. He has been repeatedly ranked among the World's Top 0.05% Scholars by ScholarGPS and the Highly Cited Chinese Researchers.

Speech Title: Deep Prior Learning Empowered Physical-Layer Signal Processing: A Transformative Paradigm for 6G

Abstract: Classical Bayesian and Shannon theories form the basis of communication design, but their application is limited by the challenge of modelling real-world wireless distributions. This talk introduces a deep-prior-learning driven physical-layer signal processing paradigm by using generative AI, particularly diffusion models, to learn probabilistic priors that enable plug-and-play posterior inference for core communication functionalities. We present two breakthrough applications: neural score-based diffusion for MIMO interference suppression, and parallel variational diffusion that redefines physical-layer processing as parallel interactive inference. Extensive results show that deep prior learning achieves paradigm-shifting performance advances in joint interference suppression, channel estimation, and MIMO detection, as well as in pilot-free MIMO-OFDM semantic communication. Free from Shannon's idealized assumptions, deep neural networks enable communication systems approaching the fundamental limits of information transfer in dynamic, interference-rich environments.

Prof. Liang Liu (IEEE Fellow), the Hong Kong Polytechnic University, China

Prof. Liang Liu is an Associate Professor at the Department of Electrical and Electronic Engineering of the Hong Kong Polytechnic University. His research interests include wireless communications and networking, advanced signal processing and optimization techniques, and Internet-of-Things (IoT). He is an IEEE ComSoc Distinguished Lecturer for the class of 2025-2026. He is the recipient of the 2021 IEEE Signal Processing Society Best Paper Award and the 2017 IEEE Signal Processing Society Young Author Best Paper Award. He is an Editor for IEEE Transactions on Wireless Communications and an Associate Editor for IEEE Transactions on Signal Processing. He is a Fellow of IEEE and Young Member of The Hong Kong Academy of Engineering.

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