Prof. Zhiguo Ding (The University of Manchester, UK)
Zhiguo Ding received his B.Eng in Electrical Engineering from the Beijing University of Posts and Telecommunications in 2000, and the Ph.D degree in Electrical Engineering from Imperial College London in 2005. From Jul. 2005 to Apr. 2018, he had been working in Queen’s University Belfast, Imperial College, Newcastle University and Lancaster University. Since Apr. 2018, he has been with the University of Manchester as a Professor in Communications. From Oct. 2012 to Sept. 2022, he has also been an academic visitor in Prof. Vincent Poor’s group at Princeton University.
Dr Ding’ research interests are machine learning, B5G networks, cooperative and energy harvesting networks and statistical signal processing. His h-index is 92 and his work receives 37,000+ Google citations. 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. 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. He received the best paper award of IET ICWMC-2009 and IEEE WCSP-2014, the EU Marie Curie Fellowship 2012-2014, the Top IEEE TVT Editor 2017, IEEE Heinrich Hertz Award 2018, IEEE Jack Neubauer Memorial Award 2018, IEEE Best Signal Processing Letter Award 2018, Alexander von Humboldt Foundation Friedrich Wilhelm Bessel Research Award 2020, and IEEE SPCC Technical Recognition Award 2021. He is a member of the Global Research Advisory Board of Yonsei University, a Web of Science Highly Cited Researcher in two disciplines (2019-2021), an IEEE ComSoc Distinguished Lecturer, and a Fellow of the IEEE.
Dr. Yuanwei Liu (Queen Mary University of London, UK)
Yuanwei Liu 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.
Yuanwei Liu is a Senior Editor of IEEE COMMUNICATIONS LETTERS, an Editor of the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS and the IEEE TRANSACTIONS ON COMMUNICATIONS. He serves as the leading Guest Editor for IEEE JSAC special issue on Next Generation Multiple Access (NGMA), a Guest Editor for IEEE JSTSP special issue on Signal Processing Advances for NOMA in Next Generation Wireless Networks. He is a Web of Science Highly Cited Researcher. He received IEEE ComSoc Outstanding Young Researcher Award for EMEA in 2020. He received the 2020 Early Achievement Award of the IEEE ComSoc SPCC and CTTC. He has served as the Publicity Co-Chair for VTC 2019- Fall. He is the leading contributor for “Best Readings for NOMA” and the primary contributor for “Best Readings for RIS”. He serves as the chair of SIG in SPCC Technical Committee on the topic of signal processing Techniques for NGMA, the vice-chair of SIG WTC on the topic of RISE, and the Tutorials and Invited Presentations Officer for Reconfigurable Intelligent Surfaces Emerging Technology Initiative.
The aim of the tutorial is to fill this gap between NOMA and NGMA via the “One Basic Principle plus Four New” concept, i.e., basic design for successive interference cancellation, new requirements, new techniques, new applications, and new tools.
Motivation and Context
As more and more new mobile multimedia rich services are becoming available to larger audiences, there is an ever-increasing demand for higher data rate wireless communications as well as larger capacity networks. This demand is to be met under the scope of next generation mobile communication systems characterized by high speed, large capacity, and good quality-of-service for millions of subscribers. The sixth generation (6G) networks need breakthroughs beyond the current 5G networks. The expected performance targets of 6G are: 1) The connectivity density is ten-fold larger compared to 5G; 2) The peak data rate reaches 1 terabit per second; 3) The energy efficiency is a hundred times higher than that of 5G; 4) The air interface latency decreases to 0.1 millisecond; and 5) The reliability increases to 99.99999%. To this end, highly efficient next-generation multiple access (NGMA) techniques are vital for 6G.
Non-orthogonal multiple access (NOMA) has been proposed to overcome the spectral inefficiency of OMA. Although NOMA has already been thoroughly investigated in the 5G and beyond networks, previous research focused on static devices and the data rate of broadband users. This ignores several fundamental problems for NGMA, e.g., the effect of mobility, the design trade-offs in terms of connectivity, reliability, and latency. Realizing the full potential of NOMA in practical communication scenarios is challenging, and there are still many important open problems that have not been solved. The aim of the tutorial is to fill this gap between NOMA and NGMA via the “One Basic Principle plus Four New” concept, i.e., basic design for successive interference cancellation, new requirements, new techniques, new applications, and new tools. This tutorial provides a good topic for PIMRC 2022 since it is closely correlated to the PHY & Fundamentals tracks.
Structure and Content
This tutorial will be presented by two senior researchers in this area (Prof. Zhiguo Ding and Dr. Yuanwei Liu) with a recorded video with well-designed slides. After the presentation, the Q&A part will be addressed online via Zoom or offline via emails. The presented materials can be sent to attendees for further learning. This process is friendly for virtual tutorials.
- Basic Principles of NOMA: Present the basics, challenges, recent progress, and open issues for NGMA:
- What will be different for 6G in terms of multiple access?
- Rethinking the importance of SIC
- New Requirements: Massive connectivity for NGMA
- QoS-based NOMA for downlink transmission
- Semi-GF NOMA for uplink transmission
- New Techniques: Integration of NGMA with emerging physical layer techniques
- Interplay between RIS and NOMA
- NOMA assisted VLC
- New Scenarios: Application of NGMA to heterogeneous scenarios
- NOMA in Integrated terrestrial and aerial networks
- NOMA in robotic communications
- New Tools: Machine learning empowered NGMA-based networks
- Reinforcement learning for NOMA-based networks
- Deep learning for NOMA-based networks
- Other machine learning for NOMA-based networks
- Outlook and discussion for research challenges and opportunities.