Speaker I

Fellow, IEEE Prof. Jingdong Chen, Northwestern Polytechnical University, China Jingdong Chen (Fellow, IEEE) received the Ph.D. degree in pattern recognition and intelligence control from the Chinese Academy of Sciences, Beijing, China, in 1998. He was with Bell Laboratories, Murray Hill, NJ, USA, WeVoice Inc., Bridgewater, NJ, Griffith University, Brisbane, QLD, Australia, and Advanced Telecommunication Research Institute International, Kyoto, Japan, for more than a decade. He is currently a Professor with Northwestern Polytechnical University, Xi'an, China. He is the co-author of a paper for which C. Pan was the recipient of the IEEE R10 (Asia-Pacific Region) Distinguished Student Paper Award, First Prize in 2016. His research interests include array signal processing, adaptive signal processing, speech enhancement, adaptive noise/echo control, signal separation, speech communication, and artificial intelligence. Dr. Chen was an Associate Editor for IEEE Transactions Audio, Speech, Language Processing from 2008 to 2014, a Technical Committee Member of IEEE Signal Processing Society TC on Audio and Electroacoustics from 2007 to 2009, and Member of IEEE SPS TC on Audio and Acoustic Signal Processing from 2018 to 2021. He is currently the Chair of IEEE Xi'an Section. He was the General Co-Chair of ACM WUWNET 2018 and IWAENC 2016, Technical Program Chair of IEEE TENCON 2013, Technical Program Co-Chair of IEEE WASPAA 2009, IEEE ChinaSIP 2014, IEEE ICSPCC 2014, and IEEE ICSPCC 2015, and helped organize many other conferences. He was the recipient of the 2008 Best Paper Award from the IEEE Signal Processing Society (with Benesty, Huang, and Doclo), Best Paper Award from the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics in 2011 (with Benesty), Bell Labs Role Model Teamwork Award twice, in 2009 and 2007, respectively, NASA Tech Brief Award twice, respectively, in 2010 and 2009, and Young Author Best Paper Award from the 5th National Conference on Man-Machine Speech Communications in 1998. He was also the recipient of the Japan Trust International Research Grant from the Japan Key Technology Center in 1998 and Distinguished Young Scientists Fund from the National Natural Science Foundation of China in 2014.

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.