Prof. Tomoaki Ohtsuki (Keio University, Japan) and Prof. Guan Gui (Nanjing University of Posts and Telecommunications, China)

With the rapid development in artificial intelligence (AI) and machine learning (ML), it can be foreseen that the future wireless communication systems will have much more intelligence than the predecessors. For problems that can be accurately modeled, traditional algorithms show good performance and efficient solutions on partially convex problems. However, for some non-convex problems, existing algorithms usually obtain more efficient solutions while allowing a certain performance loss. At this time, the ML technology is used to mine the parameter information of the known structure algorithm from the obtained data samples, to improve the convergence speed of the algorithm and the performance of the algorithm. Usually this ML-based parameter learning algorithm is called deep expansion or model-driven algorithm. However, a large number of 5G scenarios cannot be modeled by exact mathematical models such as some efficiency and latency optimization problems, resulting in the inability to obtain the accurate algorithm structure. In this case, the artificial neural networks (ANNs) including deep neural network (DNN), convolutional neural network (CNN) and so on are used to parameterize the model or algorithm, and the gradient based methods are used to optimize the NNs. These methods that obtain model or algorithm features from massive amounts of data rather than based on pre-established rules are generally called data-driven. Here we focus on the research and application of ML in physical layer. On the one hand, model based algorithms for signal detection or channel estimation can be enhanced by ML to improve the computing efficiency and system performance. On the other hand, traditional model-based methods are increasingly unable to meet the increasing demands of next-generation communication systems under the channel conditions with more complex interference and higher uncertainty. ML has the potential opportunities to redesign the baseband module including coding/decoding, detection and so on.

1. 6G
2. Deep Learning-based Wireless Communications
3. Autoencoder
4. DL-based MIMO Detection
5. Wireless Communications with DIP
6. Wireless Communications with Super Resolution
7. Wireless Communications with Trasnfer Learning
8. Wireless Communications with Meta Learning

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 215 journal papers and 415 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.

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 the Tohoku University as a research assistant as well as a postdoctoral research fellow, respectively. From 2014 to 2015, he was an Assistant Professor in the Akita Prefectural University, Akita, Japan. Since 2015, he has been a professor with Nanjing University of Posts and Telecommunications, Nanjing, China. His recent research interests include artificial intelligence, deep learning, non-orthogonal multiple access, wireless power transfer, and physical layer security.

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, 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. 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, the Nanjing Youth Award in 2018. Dr. Gui was recognized as one of the 2020 Highly Cited Chinese Researchers in wireless communications. 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, 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 VTC 2020 Spring, ISNCC 2020 and ICCC 2020, Award Chair of PIMRC 2019, and TPC member of many IEEE international conferences, including GLOBECOM, ICC, WCNC, PIRMC, VTC, and SPAWC. He is an IEEE Senior Member.