Publication
Title
AI-Empowered Management and Orchestration of Vehicular Systems in the Beyond 5G Era
Author
Abstract
The complexity of orchestrating Beyond 5G services, such as vehicular, demands novel approaches to remove limitations of existing techniques, as these might cause a large delay in orchestration operations, and thus, negatively impact the service performance. For instance, the human-in-the-loop approach is slow and prone to errors, and closed loop control using rule-based algorithms is difficult to design, as an abundant number of parameters need to be configured. Applying Artificial Intelligence (Al)/Machine Learning (ML), in combination with Network Function Virtualization (NFV) and Software Defined Networking (SDN), seems a promising solution for enabling automation and intelligence that will optimize orchestration operations. In this article, we study the challenges in current ETSI NFV orchestration solutions for B5G C-V2X edge services; propose an Al/ML-based closed-loop orchestration framework; propose how and which Al/ML techniques can alleviate the identified challenges and what are the implications resulting from applying certain Al/ML techniques; and discuss A//ML-based system enablers for B5G C-V2X services.
Language
English
Source (journal)
IEEE network. - New York, N.Y.
Publication
New York, N.Y. : 2023
ISSN
0890-8044
DOI
10.1109/MNET.008.2300024
Volume/pages
37 :4 (2023) , p. 305-313
Full text (Publisher's DOI)
Full text (open access)
The author-created version that incorporates referee comments and is the accepted for publication version Available from 24.04.2024
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identifier
Creation 08.12.2023
Last edited 09.12.2023
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