T12: What Next Generation Multiple Access Will Be?

Co-organizer: Zhiguo Ding, University of Manchester, UK
Co-organizer: Yuanwei Liu, Queen Mary University of London, UK

Abstract: Due to the explosive growth in the number of wireless devices and diverse wireless services, such as virtual/augmented reality and Internet-of-Everything, next-generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, advanced multiple access schemes are expected to be developed, namely next-generation multiple access (NGMA), which are capable of supporting massive numbers of users and network functions, e.g., communication, computation, and sensing, in a more resource- and complexity-efficient manner than existing multiple access schemes. Although the research on NGMA is in a very early stage, the trend of NGMA primarily aims to transition from orthogonality to non-orthogonality. This tutorial introduces the “One Basic Principle plus Four New” concept for designing NGMA, which begins with the basic principle by exploring possible multiple access techniques in a non-orthogonal manner. The tutorial then delves into the application of NGMA to meet the new requirements of 6G, particularly for massive connectivity in Internet-of-things networks. Next, it presents the interplay between NGMA and emerging new techniques, e.g., near-field communications, integrated sensing and communications, THz networks, age of information, and simultaneously transmitting and reflecting surfaces. Furthermore, the tutorial discusses new applications of NGMA designs, e.g., semantic communications and mobile edge computing. Finally, it investigates the use of new tools, i.e., machine learning approaches, in NGMA networks, ushering in the era of machine learning-empowered NGMA for intelligent multiple access in 6G.

 

Co-organizer’s Bios:

Zhiguo Ding

Zhiguo Ding (https://personalpages.manchester.ac.uk/staff/zhiguo.ding/index) received his Ph.D degree in Electrical Engineering from Imperial College London in 2005. Since Apr. 2018, he has been with the University of Manchester as a Professor in Communications. From Sept. 2012 to Sept. 2020, he has also been an academic visitor in Princeton University.

Dr Ding’ research interests are machine learning, B5G networks, cooperative and energy harvesting networks, and statistical signal processing. He is serving as an Area Editor for the IEEE OJ-COMS, an Editor for IEEE TVT and OJ-SP, and was an Editor for IEEE TCOM, IEEE WCL, IEEE CL and WCMC. He was the TPC Co-Chair for the 6th IET ICWMMN2015, Symposium Chair for ICNC 2016, and the 25th WOCC, and Co-Chair of WCNC-2013 Workshop on New Advances for Physical Layer Network Coding. He received the best paper award in IET Comm. Conf. on Wireless, Mobile and Computing 2009 and the International Conference on WCSP 2015, the EU Marie Curie Fellowship 2012-2014, IEEE TVT Top Editor 2017, 2018 IEEE Communication Society Heinrich Hertz Award, 2018 IEEE Vehicular Technology Society Jack Neubauer Memorial Award, and 2018 IEEE Signal Processing Society Best Signal Processing Letter Award. He is a Web of Science Highly Cited Researcher and a Fellow of the IEEE.

Yuanwei Liu

Yuanwei Liu (http://www.eecs.qmul.ac.uk/~yuanwei/index.html) received the PhD degree in electrical engineering from the Queen Mary University of London, U.K., in 2016. He was with the Department of Informatics, King’s College London, from 2016 to 2017, where he was a Post-Doctoral Research Fellow. He has been a Senior Lecturer (Associate Professor) with the School of Electronic Engineering and Computer Science, Queen Mary University of London, since Aug. 2021, where he was a Lecturer (Assistant Professor) from 2017 to 2021. His research interests include non-orthogonal multiple access, reconfigurable intelligent surface, integrated sensing and communications, and machine learning. Yuanwei Liu is a Web of Science Highly Cited Researcher since 2021, an IEEE Communication Society Distinguished Lecturer, an IEEE Vehicular Technology Society Distinguished Lecturer, and the academic Chair for the Next Generation Multiple Access Emerging Technology Initiative. He was listed as one of 35 Innovators Under 35 China in 2022 by MIT Technology Review. He received IEEE ComSoc Outstanding Young Researcher Award for EMEA in 2020. He received the 2020 IEEE Signal Processing and Computing for Communications (SPCC) Technical Early Achievement Award, IEEE Communication Theory Technical Committee (CTTC) 2021 Early Achievement Award. He received IEEE ComSoc Outstanding Nominee for Best Young Professionals Award in 2021. He is the co-recipient of the Best Student Paper Award in IEEE VTC2022-Fall, the Best Paper Award in ISWCS 2022, and the 2022 IEEE SPCC-TC Best Paper Award. He serves as the Co- Editor-in-Chief of IEEE ComSoc TC Newsletter, an Area Editor of IEEE Communications Letters, an Editor of the IEEE Transactions on Wireless Communications and the IEEE Transactions on Communications. He serves as the Guest Editor for IEEE JSAC on Next Generation Multiple Access, IEEE JSTSP on Intelligent Signal Processing and Learning for Next Generation Multiple Access, and IEEE Network on Next Generation Multiple Access for 6G. He serves as the Publicity Co-Chair for IEEE VTC 2019-Fall, Symposium Co-Chair for Cognitive Radio & AI-Enabled Networks for IEEE GLOBECOM 2022 and Communication Theory for IEEE GLOBECOM 2023. He serves as the chair of Special Interest Group (SIG) in SPCC Technical Committee on signal processing Techniques for next generation multiple access, the vice-chair of SIG WTC on Reconfigurable Intelligent Surfaces for Smart Radio Environments.