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6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

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. ISSN 0018-9219 (In Press)

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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
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: Elements
Depositing User: Elements
Date Deposited: 23 May 2022 19:38
Last Modified: 23 Sep 2022 19:54
URI: http://repository.essex.ac.uk/id/eprint/32865

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