He, Jianhua and Liu, Zilong (2022) 6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities. Proceedings of the Institute of Electrical and Electronics Engineers (IEEE), 110 (6). pp. 712-734. DOI https://doi.org/10.1109/jproc.2022.3173031 (In Press)
He, Jianhua and Liu, Zilong (2022) 6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities. Proceedings of the Institute of Electrical and Electronics Engineers (IEEE), 110 (6). pp. 712-734. DOI https://doi.org/10.1109/jproc.2022.3173031 (In Press)
He, Jianhua and Liu, Zilong (2022) 6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities. Proceedings of the Institute of Electrical and Electronics Engineers (IEEE), 110 (6). pp. 712-734. DOI https://doi.org/10.1109/jproc.2022.3173031 (In Press)
Abstract
We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.
Item Type: | Article |
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Uncontrolled Keywords: | Blockchain; brain-controlled vehicle (BCV); federated learning; intelligent reflective surfaces (IRSs); machine learning (ML); nonorthogonal multiple access (NOMA); quantum; radio frequency (RF)-visible light communication (VLC) vehicle-to-everything (V2X); sixth-generation (6G)-V2X; tactile-V2X; terahertz (THz) communications; unmanned-aerial-vehicle (UAV)/satellite-assisted V2X |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 23 May 2022 19:38 |
Last Modified: | 14 Dec 2024 02:34 |
URI: | http://repository.essex.ac.uk/id/eprint/32865 |
Available files
Filename: PI22-20220519-6G_V2X_clean.pdf