Digital Twin for 6G: Taxonomy, Research Challenges, and the Road Ahead
6G networks require the stringent quality-of-service (QoS) requirements in terms of very high data rate, ultra-high success reception rate, and very low latency. Supported by high QoS wireless communications, digital twin has become a game-changing technology in many applications including smart city, manufacturing, automotive, gaming, entertaining, and climate resilience. Edge computing-based wireless ultra-reliable and low-latency communications (URLLC) in 6G has been considered as a key technique to realise the full potential of digital twin. At the same time, Open RAN (O-RAN) Alliance is actively working towards transforming the radio access networks (RAN) industry in a way that both its physical and logical RAN products will be more open, smarter, interoperable, and scalable than contemporary deployments. In this way will be possible to address the inevitable traffic overload of the current networks caused by an expected mobile data traffic explosion, which according to ITU-R will be up to 5016 exabyte per month. Also in this context, the novel and recent concept of DT will play an important key role. Indeed, empowered with artificial intelligence (AI) and machine learning (ML) based mechanisms, DT will support the development of key functionalities of O-RAN architecture like non-real-time (Non-RT) and near real-time (Near-RT) RAN intelligent controller (RIC) modules, used to perform powerful AI aided network performance optimisations. This tutorial discusses a joint communications and computation design of URLLC multi-tier computing in 6G that supports digital twin networks, as well as a possible DT based approach for the implementation of both Non-RT and Near RT modules in O-RAN. Fundamental requirements, but also enabling technologies, visions, and future challenges will be discussed.
Dr. Trung Q. Duong (IEEE Fellow and AAIA Fellow) is a Chair Professor of Telecommunications at Queen’s University Belfast, U.K. and a Research Chair of the Royal Academy of Engineering, U.K. His current research interests include optimisation, signal processing, and machine learning in wireless communications. He has published more than 420+ published papers with 15,700+ citations and h-index 68. He has served as an Editor for many reputable IEEE journals and been awarded best paper awards in many flagship conferences. He is the recipient of the Royal Academy of Engineering Research Fellowship (2015-2020) and the prestigious Newton Prize 2017. He is a Fellow of IEEE and a Fellow of AAIA.
Dr. Antonino Masaracchia (Member, IEEE) received the Ph.D. degree in electronics and telecommunications engineering from the University of Palermo, Italy, in 2016. From 2017 to 2018, he was a Post- doctoral Researcher at the Sant’Anna School of Advanced Studies, the BioRobotics Institute. Since September 2018, he has been a Research Fellow with the Centre for Wireless Innovation, Queens University Belfast, U.K. His research interests include fifth generation (5G) and beyond 5G networks (6G) oriented services, convex optimization and applied machine learning techniques to wireless communications, reconfigurable intelligent surfaces (RIS), UAV-enabled networks, and ultra-reliable and low-latency communications (URLLC). He has been awarded with the Seal of excellence for the project proposals UAV-DRESS and UAV-SURE, submitted under the Horizon Europe Marie Skłodowska-Curie Actions in 2020 and 2021 respectively. He is actively working in collaboration with industrial partners in the context of Open RAN (ORAN) architecture.