Speaker I

Fellow, IEEE Prof. Hongbin Li, Stevens Institute of Technology, USA Hongbin Li is the Charles and Rosanna Batchelor Memorial Chair Professor at the Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA. His general research interests include statistical signal processing, machine learning, radars, and wireless communications. He was a member of the SPTM and SAM technical committees of the IEEE Signal Processing Society. He served on the editorial boards for IEEE Transactions on Wireless Communications, IEEE Signal Processing Letters, IEEE Transactions on Signal Processing, and Elsevier Signal Processing, and was involved in guesting editing for several IEEE and non-IEEE journals. He received a number of research recognitions including the IEEE Jack Neubauer Memorial Award in 2013, Provost’s Award for Research Excellence in 2019, and an honorary Master of Engineering (Honoris Causa) degree from Stevens Institute of Technology in 2024. He is a Fellow of IEEE, International Artificial Intelligence Industry Alliance (AIIA), and Asia-Pacific Artificial Intelligence Association (AAIA). Speech Title: Look Around the Corner: Ubiquitous RF Sensing with Reconfigurable Intelligent Surfaces Abstract: The proliferation of wireless communications has made radio frequency (RF) sensing increasingly ubiquitous. Remarkably, without requiring dedicated radio transmitters, RF sensing technologies enable us to leverage ambient wireless sources, such as cellular and WiFi signals, to illuminate the environment and sense surroundings with a simple mobile device. However, most RF sensors are unable to look around the corner and locate non-line-of-sight (NLOS) targets. For NLOS RF sensing, a traditional approach involves deploying multiple transmitters and receivers across the surveillance area. The resulting system is a distributed radar, which is bulky, expensive, and environmentally unfriendly due to excessive RF emissions. The rise of reconfigurable intelligent surface (RIS) offers a lightweight, low-cost, and energy-efficient solution to the NLOS sensing problem. A planar structure with numerous adjustable metamaterial elements, RIS was first embraced by the wireless communication community for its ability to control the radio environment. In this talk, we explore new NLOS RF sensing opportunities with RIS, discussing fundamental signal processing related issues and explaining how to optimize a RIS-aided system across various sensing scenarios and system configurations.

Speaker II

Fellow, IEEE Prof. Guan Gui, Nanjing University of Posts and Telecommunications, China Guan Gui (Fellow, IEEE) 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. Since 2022, he has been a Distinguished Lecturer of the IEEE Vehicular Technology Society. He is serving or served on the editorial boards of several journals, including IEEE Transactions on Vehicular Technology, IEICE Transactions on Communications, Physical Communication, Wireless Networks, IEEE Access, Journal of Circuits, Systems and Computers, Security and Communication Networks, IEICE Communications Express, and KSII Transactions on Internet and Information Systems, and Journal on Communications. In addition, he served as the IEEE VTS Ad Hoc Committee Member in AI Wireless; the Executive Chair of IEEE ICCT 2023; the Workshop Chair of LANTINCOM2023; the TPC Chair of PRAI 2022, ICGIP 2022, PHM 2021, and WiMob 2020; the Executive Chair of VTC 2021-Fall; the Vice Chair of WCNC 2021; the Symposium Chair of WCSP 2021; the General Co-Chair of Mobimedia 2020; the Track Chairs of EuCNC 2021 and 2022, and VTC 2020 Spring; the Award Chair of PIMRC 2019; and a TPC Member of many IEEE international conferences, such as GLOBECOM, ICC, WCNC, PIRMC, VTC, and SPAWC. Speech Title: Intelligent Signal Sensing and Recognition Towards Physical Security 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.