Founding Chair: Yuanwei Liu, Queen Mary University of London, UK (yuanwei.liu@qmul.ac.uk)
Vice-chairs: Zhiguo Ding, The University of Manchester, UK (zhiguo.ding@manchester.ac.uk)
Octavia A. Dobre, Memorial University of Newfoundland, Canada (odobre@mun.ca)
Kanapathippillai Cumanan, University of York, UK (kanapathippillai.cumanan@york.ac.uk)
Secretary: Xidong Mu, Queen Mary University of London, UK (xidong.mu@qmul.ac.uk)
About the SIG: User data traffic, especially large amounts of video traffic and small-size Internet-of-things (IoT) packets, have dramatically increased in recent years with the emergence of smart devices, smart sensors and various new applications such as augmented reality (AR), virtual reality (VR), holographic telepresence, industry 4.0, and robotics. It is hence crucial to increase network capacity and user access to accommodate bandwidth-hungry applications and enhance massive connectivity. Next-generation multiple access (NGMA) has received considerable attention in both industry and academia for addressing the dramatically increasing demand for massive user access, heterogeneous data traffic, high bandwidth efficiency, and low latency services. The key concept of NGMA will be the accommodation of multiple users in the allotted resource blocks, such as time slots, frequency bands, spreading codes, and power levels, in the most effective manner. By doing so, high bandwidth efficiency and large-scale user connectivity can be attained.
In order to enlarge the massive connectivity and support overloading transmission, NGMA requires advanced signal processing techniques to mitigate the interference with low complexity. Furthermore, NGMA has to facilitate efficient random access for delay-sensitive IoT applications, such as industry 4.0 and robotics. In addition, recent advances in machine learning and big data-enabled signal processing techniques promise significant advantages in terms of the performance-complexity tradeoff. Recent advances in hardware, theory, and machine learning, provide novel solutions for signal processing in NGMA to obtain affordable signal processing complexity and acceptable computation latency. However, realizing the full potential of NGMA in practical communication scenarios is challenging, and there are still many important open problems that have not been solved, for instance, in scenarios where the users have heterogeneous mobility, which makes them difficult to access to the networks. In addition, in NGMA, sophisticated digital signal processing algorithms for multi-user detection and interference control have to be developed for successful implementation in next-generation wireless systems. The main focus of this special interest group (SIG) is to provide advance signal processing for promising NGMA techniques, address research challenges of connecting massive devices, and deliver state-of-the-art signal processing approaches, such as machine learning and big data, for intelligent multiple access in next-generation wireless networks.
Scope: With the above vision, this proposed Special Interest Group (SIG) within the Signal Processing and Computing for Communications (SPCC) technical committee of the IEEE Communications Society aims at promoting, coordinating, and supporting research activities related to NGMA, discussing possible candidates for multiple access techniques towards 6G, to further enhance spectral efficiency and support massive connectivity. The SIG will constitute a platform for gathering together the latest and most promising research advances.
Activities:
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