Title: Deep Learning Aided Intelligent Sensing and Identification for 6G

Speakers: Tomoaki Otsuki and Guan Gui

Duration: haft-day

Abstract: With the rapid advancements in artificial intelligence (AI) and deep learning (DL), it is evident that future wireless communication systems will possess significantly enhanced intelligence and security compared to their predecessors. While traditional algorithms demonstrate commendable performance and efficiency in addressing accurately modeled problems, they often face challenges when dealing with non-convex problems, leading to compromised performance. In such scenarios, DL technology can be employed to extract parameter information from acquired data samples, thereby improving algorithm convergence speed and performance. This tutorial aims to explore the utilization of artificial neural networks (ANNs), including deep neural networks (DNNs) and convolutional neural networks (CNNs), to parameterize models or algorithms, with gradient-based methods utilized for optimizing the ANNs. These data-driven techniques, which derive model or algorithm features from massive amounts of data rather than relying on pre-established rules, are collectively known as data-driven methods. The tutorial primarily focuses on the research and application of DL in the physical layer of wireless communication systems. Firstly, DL can enhance model-based algorithms for signal detection or channel estimation, improving computational efficiency and system performance. Secondly, as traditional model-based methods increasingly struggle to meet the growing demands of next-generation communication systems operating in complex interference and uncertain channel conditions, DL presents promising opportunities to redesign baseband modules, including coding/decoding and detection, among others.

Tutorial outline:

  • Background and Motivation
  • 6G
  • Deep Learning based Wireless Communications
  • DL-based MIMO Detection
  • Wireless Communications with DIP
  • Wireless Communications with Super Resolution
  • Wireless Communications with Transfer Learning
  • Deep Learning based Inter-distance Estimation
  • Deep Learning based Automatic Modulation Classification (AMC)
  • Deep Learning based Specific Emitter Identification (SEI)
  • Deep Learning based Channel State Information (CSI) Inferring
  • Deep Learning based Beamforming Design Methods
  • Deep Learning based Intrusion Detection Methods
  • Summary and Future Work

Speakers’ Biography:

Tomoaki Otsuki (Ohtsuki) received the B.E., M.E., and Ph. D. degrees in Electrical Engineering from Keio University, Yokohama, Japan in 1990, 1992, and 1994, respectively. He is now a Professor at Keio University. He has published more than 235 journal papers and 460 international conference papers. He served as a Chair of IEEE Communications Society, Signal Processing for Communications and Electronics Technical Committee. He served as a technical editor of the IEEE Wireless Communications Magazine and an editor of Elsevier Physical Communications. He is now serving as an Area Editor of the IEEE Transactions on Vehicular Technology and an editor of the IEEE Communications Surveys and Tutorials. He has served as general-co chair, symposium co-chair, and TPC co-chair of many conferences, including IEEE GLOBECOM 2008, SPC, IEEE ICC 2011, CTS, IEEE GLOBECOM 2012, SPC, IEEE ICC 2020, SPC, IEEE APWCS, IEEE SPAWC, and IEEE VTC. He gave tutorials and keynote speeches at many international conferences including IEEE VTC, IEEE PIMRC, IEEE WCNC, and so on. He was Vice President and President of the Communications Society of the IEICE. He is a senior member and a distinguished lecturer of the IEEE, a fellow of the IEICE, and a member of the Engineering Academy of Japan.

Dr. Gui has published more than 200 IEEE Journal/Conference papers and won several best paper awards, e.g., 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 in 2021, the Clarivate Analytics Highly Cited Researcher in Cross-Field in 2021, the Highly Cited Chinese Researchers by Elsevier in 2020 and 2021, the 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 for the Jiangsu Specially-Appointed Professor in 2016, the Jiangsu High-level Innovation and Entrepreneurial Talent in 2016, the Jiangsu Six Top Talent in 2018. Since 2022, he has been a Distinguished Lecturer of the IEEE Vehicular Technology Society. He is a Senior Member of the IEEE, a Member of the IEEE Communications Society and 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, Journal on Communications. In addition, he served as the IEEE VTS Ad Hoc Committee Member in AI Wireless, TPC Chair of PRAI 2022, TPC Chair of ICGIP 2022,  Executive Chair of VTC 2021-Fall, Vice Chair of WCNC 2021, TPC Chair of PHM 2021, Symposium Chair of WCSP 2021, General Co-Chair of Mobimedia 2020, TPC Chair of WiMob 2020, Track Chairs of EuCNC 2021 and 2022, VTC 2020 Spring, Award Chair of PIMRC 2019, and TPC member of many IEEE international conferences, including GLOBECOM, ICC, WCNC, PIRMC, VTC, and SPAWC.