Prof. Xianbin Wang, IEEE Fellow & Fellow of Canadian Academy of Engineering
Western University, Canada

Dr. Xianbin Wang is a Distinguished University Professor and a Tier-1 Canada Research Chair in Trusted Communications and Computing with Western University, Canada. His current research interests include 5G/6G technologies, Internet of Things, machine learning, communications security, and intelligent communications. He has over 700 highly cited journals and conference papers, in addition to over 30 granted and pending patents and several standard contributions.
Dr. Wang is a Fellow of IEEE, a Fellow of the Canadian Academy of Engineering and a Fellow of the Engineering Institute of Canada. He has received many prestigious awards and recognitions, including the IEEE Canada R. A. Fessenden Award, Canada Research Chair, Engineering Research Excellence Award at Western University, Canadian Federal Government Public Service Award, Ontario Early Researcher Award, and 10 Best Paper Awards. He is currently a member of the Senate, Senate Committee on Academic Policy and Senate Committee on University Planning at Western. He has been involved in many flagship conferences, including GLOBECOM, ICC, VTC, PIMRC, WCNC, CCECE, and ICNC, in different roles, such as General Chair, TPC Chair, Symposium Chair, Tutorial Instructor, Track Chair, Session Chair, and Keynote Speaker. He serves/has served as the Editor-in-Chief, Associate Editor-in-Chief, and editor/associate editor for over ten journals. He has served on the IEEE Fellow Committee and the Fellow Committee of IEEE Communications Society. He was the Chair of the IEEE ComSoc Signal Processing and Computing for Communications (SPCC) Technical Committee and is currently serving as the Central Area Chair of IEEE Canada.

Speech Title: Intelligent Trust Provisioning and Collaborative Task Completion in the Era of 6G and Generative AI

Abstract: The rapid evolution of digital technologies from 1G to 6G, coupled proliferation networked system, has given rise to a wide variety of complex tasks that can only be executed by distributed devices collaboratively. In effectively completing such complex tasks, a core challenge lies in dynamically aligning diverse task-specific requirements with the capabilities, reliability and conditions of potential collaborators through intelligent trust evaluation. This keynote will explore the critical aspects of intelligent trust evaluation and collaborator selection for collaborative task completion. Specifically, this presentation will cover: i) Evolving challenges in trusted collaboration in networked systems, including diverse task requirements, task-specific definitions of trust, and their impact on effective task completion. ii) Key enabling technologies and mathematical frameworks for task-specific trust evaluation, trusted collaborator selection, and effective task completion. iii) Generative AI-driven autonomous trust orchestration, based on a new concept of semantic chain-of-trust. Agentic AI and hypergraph models will be discussed as tools to establish, maintain, and adapt spatiotemporal trust relationships among devices for effective collaboration and task completion.

Prof. Yao Yu, Northeastern University, China

Director, Department of Communication and Electronic Engineering, School of Computer Science and Engineering, Northeastern University

Yao Yu (Senior Member, IEEE) received the B.S. degree in communication engineering and the Ph.D. degree in communication and information system from Northeastern University, Shenyang, China, in 2005 and 2010, respectively. From 2010 to 2011, she was a Postdoctoral Fellow with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, China. She was also a Visiting Scholar with The University of Sydney, Sydney, NSW, Australia, from 2019 to 2020. She is currently a Professor with the School of Computer Science and Engineering, Northeastern University. Her current research interest is intelligent wireless communications.

Speech Title: Bio-inspired Converged Network
Abstract: Future autonomous swarm networks are rapidly evolving toward heterogeneity, intelligence, and large-scale collaboration.  However, existing centralized and homogeneous architectures struggle to support cross-domain, cross-platform cooperation under highly dynamic and resource-constrained environments.  To address these challenges, we introduce the Bio-inspired Converged Networks (BiCN). This unified framework draws inspiration from the structural, functional, and behavioral mechanisms of biological systems, particularly the immune system’s surveillance, response, recognition, and memory processes.

BiCN aims to integrate heterogeneous unmanned systems into a collaborative network capable of self-organizing sensing, self-adaptive communication, and self-evolving decision-making. We present three major research directions: the multi-source cooperative sensing for diversified information, the high-efficiency adaptive communication for dynamic environments, and the intelligent consistent decision-making across heterogeneous agents. We further propose immune-inspired approaches for cooperative sensing, efficient communication, and intelligent decision-making, thereby enhancing collaboration among heterogeneous networks.

Overall, BiCN provides a bio-inspired framework for building resilient, autonomous, and evolvable networks, supporting future 6G, computing-driven infrastructures, and integrated space–air–ground–sea systems.

Prof. Nan Zhao, Dalian University of Technology, China

Young Changjiang Scholar

Nan Zhao, Professor at Dalian University of Technology, Young Changjiang Scholar of the Ministry of Education, specializing in wireless communication and networks. He has been recognized as a Highly Cited Researcher globally, received the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award, the Youth Science and Technology Award of the China Institute of Communications, the Liaoning Natural Science Award, and the Technological Invention Award of the China Institute of Communications. His Google Scholar citations exceed 21,000. He has led numerous research projects, including key topics under the National Key R&D Program and the National Natural Science Foundation Regional Joint Key Fund. He has served as an editorial board member for over 10 prestigious international and domestic journals, such as IEEE Communications Surveys & Tutorials.

Speech Title: Theory and Methods for Low-Altitude Covert Communication
Abstract: The low-altitude economy is developing rapidly. However, the low-altitude communication environment is complex and variable, facing severe challenges such as co-channel interference, malicious jamming, and risks of unintended detection or intentional interception. Traditional communication technologies, focused primarily on ensuring reliable transmission, struggle to meet the urgent modern low-altitude application demands for being "invisible yet transmitting flawlessly." This presentation will focus on the frontier field of low-altitude covert communication, systematically elaborating on its core theories and key technologies. Building on this foundation, it will provide detailed introductions to key research directions, including air-ground coordination, intelligent reflecting surfaces, integrated sensing and communication, and artificial intelligence for low-altitude covert communication. Finally, the application prospects and development trends of low-altitude covert communication technologies will be discussed.

Prof. Meng Zhang, Xi’an Jiaotong University, China

National-level Young Talent

Meng Zhang is a professor and doctoral supervisor at Xi'an Jiaotong University, a Chief Scientist of the National Key R&D Program and young Changjiang Scholar from the Ministry of Education. Meng Zhang obtained his Ph.D degree from the school of control science and engineering of zhejiang university. Meng Zhang has received honors such as the First Prize for Excellent Achievements in Science and Technology Research at Shaanxi Higher Education Institutions, the First Prize for Natural Science at the Chinese Association of Automation, the Outstanding Youth Award for Artificial Intelligence of Wu Wenjun. Meng Zhang has published more than80 papers in journals such as Automatica Full Paper, IEEE TAC Full Paper, IEEE TASE, etc. Meng Zhang serves as an associate editor of IEEE Transactions on Cybernetics, IEEE Transactions on Automation Science and Engineering and the chairman of IEEE IESONCON and other conference industrial forums. Meng Zhang’s research directions include intelligent control and optimization with applications to robotics and smart grids.

Speech Title: Power System Control Driven by Reinforcement Learning
Abstract: As the complexity of power systems continues to increase, purely model-based control methods struggle to effectively address the control challenges of complex power systems. Therefore, reinforcement learning, as one of the promising data-driven approaches, has been extensively studied and applied to solve these problems. This report first introduces how to leverage Lyapunov theory and stable deep dynamic models to ensure the stability of the system's equilibrium points, and optimizes the model through a deep reinforcement learning architecture to enhance the control performance of grid-forming and grid-connected inverters. Next, a multi-agent deep reinforcement learning framework combining offline training and online learning is designed, a model-free power system control method is proposed, and adaptive multi-area power system cooperative control is realized to address system uncertainties caused by renewable energy sources and other factors. Finally, experimental results demonstrate that the reinforcement learning-driven control method can achieve superior control performance compared with traditional methods under various power grid conditions and disturbances.