Speech Title: Optimization by Learning and Federated Learning for Communication Networks

Abstract: Allocation of network resources to competing demands is an important problem for efficient design and management of future communication networks. The complexity of the issue is compounded by system dynamics in terms of fluctuation of resource demands and availability. On the future communication networks, users do not expect them to support only conventional multi-media services, but also future artificial intelligence (AI) and machine-learning (ML) applications for sensing and communications.

In the first part of this speech, the speaker will discuss the issue of network resource allocation. Specifically, he will present a new machine-learning method by using two Coupled Long Short-Term Memory (CLSTM) networks to quickly and robustly produce the optimal or near-optimal resource allocation, which is modeled as constrained optimization problem, over a range of system parameters. Numerical examples for allocation of network resources will be presented to confirm the validity of the proposed method.

In the second part, the speaker will present new approaches to supporting federated learning (FL) and improving the learning process by model pruning in communication networks with resource constraints. The FL technique learns the model parameters from data collected at distributed nodes and adapts according to the limited availability of resources. The key idea of model pruning is to remove unimportant model parameters to reduce computation and communication burden and speed up the learning convergence, while maintaining the model accuracy. Using real datasets, the experimentation results show that the proposed approaches perform near to the optimum or offer significant performance improvement over other methods.

Bio: Kin K. Leung received his B.S. degree from the Chinese University of Hong Kong, and his M.S. and Ph.D. degrees from University of California, Los Angeles. He worked at AT&T Bell Labs and its successor companies in New Jersey from 1986 to 2004. Since then, he has been the Tanaka Chair Professor in the Electrical and Electronic Engineering (EEE), and Computing Departments at Imperial College in London. He also served as the Head of Communications and Signal Processing Group in the EEE Department at Imperial from 2009 to 2024. His current research focuses on optimization and machine learning for system design and control of large-scale communications, computer and quantum networks. He also works on multi-antenna and cross-layer designs for wireless networks.

He is a Fellow of the Royal Academy of Engineering, IEEE Fellow, IET Fellow, and member of Academia Europaea. He received the Distinguished Member of Technical Staff Award from AT&T Bell Labs (1994) and the Royal Society Wolfson Research Merits Award (2004-09). Jointly with his collaborators, he received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize (2021), the IEEE ComSoc Best Survey Paper Award (2022), the U.S.–UK Science and Technology Stocktake Award (2021), the Lanchester Prize Honorable Mention Award (1997), and several best conference paper awards. He was an IEEE ComSoc Distinguished Lecturer (2022-23). He was a member (2009-11) and the chairman (2012-15) of the IEEE Fellow Evaluation Committee for the ComSoc. He has served as an editor for 10 IEEE and ACM journals and chaired the Steering Committee for the IEEE Transactions on Mobile Computing. Currently, he is an editor for the ACM Computing Survey and International Journal of Sensor Networks.

 

Speech Title: mmWave Integrated Communications and Sensing

Abstract: Integrated communications and sensing (ISAC) has become very popular for the next generation mobile communications. This seminar will introduce the concept and challenges of using milimeter wave (mmWave) for ISAC. The latest research results in mmWave ISAC will be presented in conjunction with hybrid beamforming and rate splitting multiple access technologies.

Bio: Jiangzhou Wang is a Professor with the University of Kent, U.K. He has published more than 500 papers and five books. His research interest is in mobile communications. He was a recipient of the 2022 IEEE Communications Society Leonard G. Abraham Prize. He was the Technical Program Chair of the 2019 IEEE International Conference on Communications (ICC2019), Shanghai, Executive Chair of the IEEE ICC2015, London, and Technical Program Chair of the IEEE WCNC2013. He is/was the editor of multiple international journals, including IEEE Transactions on Communications from 1998 to 2013. Professor Wang is an International Member of the Chinese Academy of Engineering (CAE), a Fellow of the Royal Academy of Engineering (RAEng), U.K., Fellow of the IEEE, and Fellow of the IET.

 

Speech Title: Some Thoughts on 6G Modulation

Abstract: I will talk about some of my own thoughts on 6G modulation. I think that 6G modulation should be a trade-off between complexity and performance. Two extremes are OFDM and single carrier frequency domain equalizer (SC-FDE). I will briefly introduce vector OFDM (VOFDM) that is in the middle of the two, and is a natural trade-off of complexity and performance, in particular for time-varying channels (delay Doppler channels).

Bio: Xiang-Gen Xia is the Charles Black Evans Professor, Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, USA. Dr. Xia was the Kumar’s Chair Professor Group Professor (guest) in Wireless Communications, Tsinghua University, during 2009-2011, the Chang Jiang Chair Professor (visiting), Xidian University, during 2010-2012, and the World Class University (WCU) Chair Professor (visiting), Chonbuk National University, South Korea, during 2009-2013. He received the National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program Award in 1997, the Office of Naval Research (ONR) Young Investigator Award in 1998, the Outstanding Overseas Young Investigator Award from the National Nature Science Foundation of China in 2001, and the Information Theory Outstanding Overseas Chinese Scientist Award from the Chinese Information Theory Society of Chinese Institute of Electronics in 2019. Dr. Xia was the General Co-Chair of ICASSP 2005 in Philadelphia. He is a Fellow of IEEE. His current research interests include space-time coding, MIMO and OFDM systems, digital signal processing, and SAR and ISAR imaging. He is the author of the book Modulated Coding for Intersymbol Interference Channels (New York, Marcel Dekker, 2000) and a co-author of the book Array Beamforming Enabled Wireless Communications (New York, CRC Press, 2023).

 

Speech Title: Intelligent Signal Sensing and Recognition Techniques Towards 6G

Abstract: The dawn of 6G wireless communication introduces a transformative era characterized by pervasive sensing and advanced intelligent identification, essential for ensuring physical security. This keynote speech highlights the integration of Artificial Intelligence (AI) and Deep Learning (DL) as pivotal in addressing the dynamic and complex challenges of 6G networks. We emphasize the role of AI in revolutionizing signal sensing and recognition. Our discussion centers on the application of these neural networks in enhancing signal detection, classification, and Specific Emitter Identification (SEI). By leveraging gradient-based optimization techniques, we demonstrate how ANNs can improve model and algorithm parameterization, leading to a data-driven approach that surpasses traditional rule-based systems. This advancement is crucial in the physical layer of wireless communications, where intelligent signal recognition plays a key role in maintaining security and efficiency. We also explore the challenges faced by conventional model-based methods in the evolving landscape of 6G communication systems, which are marked by complex interference and uncertain channel conditions. DL emerges as a solution, offering innovative strategies for redesigning baseband module functionalities, including coding/decoding and detection processes. In conclusion, this keynote underscores the significance of integrating intelligent signal sensing and recognition with DL technologies in 6G networks. This approach not only enhances physical security but also paves the way for a more robust, efficient, and intelligent wireless communication ecosystem, capable of meeting the security demands of the future.

Bio: Guan Gui received the Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2012. From 2009 to 2014, he joined Tohoku University as a Research Assistant and a Post-Doctoral Research Fellow. From 2014 to 2015, he was an Assistant Professor with Akita Prefectural University, Akita, Japan. Since 2015, he has been a Professor with the Nanjing University of Posts and Telecommunications, Nanjing, China. He has published more than 200 IEEE journals/conference papers. His recent research interests include intelligence sensing and recognition, intelligent signal processing, and physical layer security. Dr. Gui contributions to intelligent signal analysis and wireless resource optimization have earned him the title of fellow of the IEEE, IET, and AAIA. He was a recipient of several Best Paper Awards, such as ICC 2017, ICC 2014, and VTC 2014-Spring. He received the IEEE Communications Society Heinrich Hertz Award in 2021, top 2% scientists of the world by Stanford University from 2021 to 2023, the Clarivate Analytics Highly Cited Researcher in Cross-Field from 2021 to 2023, the Highly Cited Chinese Researchers by Elsevier from 2020 to 2023, a member and Global Activities Contributions Award in 2018, the Top Editor Award of IEEE Transactions on Vehicular Technology in 2019, the Outstanding Journal Service Award of KSII Transactions on Internet and Information System in 2020, the Exemplary Reviewer Award of IEEE Communications Letters in 2017, the 2012 Japan Society for Promotion of Science (JSPS) Postdoctoral Fellowships for Foreign Researchers, and the 2018 Japan Society for Promotion of Science (JSPS) International Fellowships for Overseas Researchers. He was also selected as the Jiangsu Specially-Appointed Professor in 2016, the Jiangsu High-Level Innovation and Entrepreneurial Talent in 2016, and the Jiangsu Six Top Talent in 2018.