Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received tremendous research attention. However, realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives, while with single CG (SCG), UE need to wait for the CG period to transmit the packet. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. However, in the GF-NOMA scheme, the data is transmitted along with the pilot randomly, which is unknown at the BS and can lead to new research problems. In this talk, Machine Learning (ML) approaches in mURLLC systems will be presented. Promising research directions and possible ML solutions will also be discussed.
Arumugam Nallanathan is Professor of Wireless Communications and the founding head of the Communication Systems Research (CSR) group in the School of Electronic Engineering and Computer Science at Queen Mary University of London since September 2017. He was with the Department of Informatics at King’s College London from December 2007 to August 2017, where he was Professor of Wireless Communications from April 2013 to August 2017. He was an Assistant Professor in the Department of Electrical and Computer Engineering, National University of Singapore from August 2000 to December 2007. His research interests include 6G Wireless Networks, Internet of Things (IoT) and Molecular Communications. He published more than 500 technical papers in scientific journals and international conferences. He is a co-recipient of the Best Paper Awards presented at the IEEE International Conference on Communications 2016 (ICC’2016), IEEE Global Communications Conference 2017 (GLOBECOM’2017) and IEEE Vehicular Technology Conference 2017 (VTC’2017). He is a co-receipient of IEEE Communications Society Leonard G. Abraham Prize, 2022.