Xianbin Wang (Fellow, IEEE) received his Ph.D. degree in electrical and computer engineering from the National University of Singapore in 2001.
He is a Professor and a Tier-1 Canada Research Chair in 5G and Wireless IoT Communications with Western University, Canada. Prior to joining Western University, he was with the Communications Research Centre Canada as a Research Scientist/Senior Research Scientist from 2002 to 2007. From 2001 to 2002, he was a System Designer at STMicroelectronics. His current research interests include 5G/6G technologies, Internet of Things, machine learning, communications security, and intelligent communications. He has over 600 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 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 nine Best Paper Awards. He was involved in many IEEE conferences, including GLOBECOM, ICC, VTC, PIMRC, WCNC, CCECE, and CWIT, 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 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.
Title: 6G: Beyond Communications Capabilities, Enabling Technologies, and Emerging Vertical Applications
Abstract: The massive deployment of the fifth generation (5G) wireless networks are significantly accelerating the ongoing process of industrial and societal transformation. Disregard many impressive achievements, current scenario-specific, and communication-centric 5G technologies still face many challenges in empowering future applications with diverse requirements. The fundamental design consideration of the sixth generation (6G) networks is how to identify and meet its application-specific operational goals with beyond communications capabilities and constrained resources.
The focus of this presentation is to analyze the technical challenges of 6G, identify the essential key enabling technologies, and present the related ongoing research activities and future development directions. Different perspectives of the 6G networks, including the 6G operational goals, key performance indicators (KPIs), and emerging technologies, are analyzed and presented. Our recent research activities on 6G including multi-dimensional multiple access, network synchronization, integrated sensing and communication as well as value-oriented operations will be presented.

Prof. Kun Yang University of Essex, UK IEEE Fellow, IET Fellow, ACM Distinguished Member Member of Academia Europaea (MAE)

Kun Yang received his PhD from the Department of Electronic & Electrical Engineering of University College London (UCL), UK. He is currently a Chair Professor in the School of Computer Science & Electronic Engineering, University of Essex, UK, leading the Network Convergence Laboratory (NCL). He is also an affiliated professor of Nanjing University, China. His main research interests include wireless networks and communications, future Internet and edge computing. In particular he is interested in energy aspects of future communication systems and AI for wireless. He has managed research projects funded by UK EPSRC, EU FP7/H2020, and industries. He has published 400+ papers and filed 20 patents. He serves on the editorial boards of a number of IEEE journals (e.g., IEEE ComMag, TNSE, TVT). He is a Deputy Editor-in-Chief of IET Smart Cities Journal. He has been a Judge of GSMA GLOMO Award at World Mobile Congress – Barcelona since 2019. He was a Distinguished Lecturer of IEEE ComSoc (2020-2021).
He is a Member of Academia Europaea (MAE), a Fellow of IEEE, a Fellow of IET and a Distinguished Member of ACM.
Title: AI-enabled Self-driving Communication Networks
Abstract: Modern Artificial Intelligence (AI) has proven to be a powerful enabler that has gained success in many vertical fields. There is a clear evidence of determined effort in the communication and network community to explore the AI power to deliver 6G mobile network’s promises of being faster, greener and smarter. This talk starts with a brief introduction of 6G mobile communication systems, and then looks into how new AI technologies, and in particular machine learning, come into play in 6G from different perspectives. It covers new trends in 6G communication research such as data-driven communication system design, semantic communications, digital twin networks (DTN), and large model for wireless networks. One major objective of these researches is to achieve self-driving communication networks where lengthy standardization of such as communication waveforms or protocol design can be somehow reduced or even eliminated, thus enabling 6G to self-drive to versatile requirements from vertical industries.

Prof. Lie-Liang Yang University of Southampton, UK IEEE Fellow, IET Fellow, AAIA Fellow

Lie-Liang Yang is the professor of Wireless Communications in the School of Electronics and Computer Science at the University of Southampton, UK. He received his MEng and PhD degrees in communications and electronics from Northern (Beijing) Jiaotong University, Beijing, China in 1991 and 1997, respectively, and his BEng degree in communications engineering from Shanghai TieDao University, Shanghai, China in 1988. He has research interest in wireless communications, wireless networks and signal processing for wireless communications, as well as molecular communications and nano-networks. On these research topics, he has graduated 35 PhD students and currently supervises 5 PhD students, and has also supervised 150+ master projects. He has published 400+ research papers in journals and conference proceedings, authored/co-authored four books and also published 10+ book chapters. The details about his research publications can be found at https://www.ecs.soton.ac.uk/people/llyang. He is a fellow of the IEEE, IET and the AAIA, and was a distinguished lecturer of the IEEE VTS. He served as an associate editor to various journals, and is currently a senior editor to the IEEE Access and a subject editor to the Electronics Letters. He also acted different roles for organization of conferences.
Title: 6G Physical-Layer Multiplexing Signaling
Abstract: 6G wireless systems are anticipated to support a wide range of services, including various communication and sensing services, across diverse environments. Therefore, a flexible and reconfigurable physical-layer multiplexing signaling scheme is crucial for meeting potential challenges while maintaining high efficiency. This talk will begin by reviewing the state-of-the-art physical-layer multiplexing schemes employed in 1G through 5G. Next, we will explore some candidate multiplexing signaling schemes proposed for 6G to address these anticipated challenges. Finally, we will propose and analyze a reconfigurable multiplexing signaling scheme designed to combine all the advantages of the proposed 6G candidates while maintaining compatibility with existing 4G/5G multiplexing signaling schemes.

Prof. Shuanghua Yang University of Reading, UK IET Fellow, IEEE Senior Member

Shuang-Hua Yang received his BSc degree in instrument and automation and the MSc degree in process control from the China University of Petroleum (Huadong), Beijing, China, in 1983 and 1986, respectively, and the PhD degree in intelligent systems from Zhejiang University, Hangzhou, China, in 1991. He is currently professor and the Head of Department of Computer Science at University of Reading, UK, and the Director of the Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, based at the Southern University of Science and Technology, China. His research interests include cyber-physical systems, the Internet of Things, wireless network-based monitoring and control, and safety-critical systems. He is a fellow of IET and InstMC, UK, and a senior member of IEEE. He was awarded a Doctor of Science, degree, a higher doctorate degree, in 2014 from Loughborough University to recognize his scientific achievement in his academic career. He was awarded the 2010 Honeywell Prize by the Institute of Measurement and Control in the UK in recognition of his contribution to home automation research. He is also an Associate Editor of IET Cyber-Physical Systems: Theory and Applications.
Title: Accident identification and localization for water distribution networks
Abstract: Efficient management of water distribution networks (WDNs) is crucial for ensuring water quality and mitigating losses from contamination and leaks. Traditional methods for contamination source identification (CSI) encounter computational challenges, while leak detection techniques struggle with multi-leak scenarios, and localization methods face challenges in achieving precise real-time results. To address these limitations, we propose DLGEA, a novel framework integrating deep learning and evolutionary algorithms for CSI, and a leak detection and localization framework via online change-point detection and leak sensitivity modeling. DLGEA utilizes a deep neural network (DNN) model trained on simulated contamination events to guide evolutionary algorithms by optimising the search space, thus enhancing CSI efficiency. The proposed leak detection and localization framework integrates 1D-convolutional auto-encoder (1D-CAE) with an online changepoint detector, sequentially discounting normalized maximum likelihood (SDNML), for real-time detection in multi-leak scenarios. Leveraging leak sensitivity modeling and 1D-CAE, it enhances localization accuracy while minimizing computational cost. Evaluation on benchmark datasets demonstrates the superior performance of our frameworks, showcasing its potential for enhancing WDN management and security.