Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey

Cellular Vehicle-to-everything (C-V2X) communication is critical for Intelligent Transportation Systems (ITS), facilitating information exchange among road users and infrastructure. Since its first introduction in rel-15 by 3GPP, 5G NR-V2X features have continued to evolve, aiming to support increas...

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Main Authors: Chaeriah Bin Ali Wael, El Hadj Dogheche, Nasrullah Armi, Agus Subekti, Iyad Dayoub
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of Intelligent Transportation Systems
Subjects:
Online Access:https://ieeexplore.ieee.org/document/11054065/
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author Chaeriah Bin Ali Wael
El Hadj Dogheche
Nasrullah Armi
Agus Subekti
Iyad Dayoub
author_facet Chaeriah Bin Ali Wael
El Hadj Dogheche
Nasrullah Armi
Agus Subekti
Iyad Dayoub
author_sort Chaeriah Bin Ali Wael
collection DOAJ
description Cellular Vehicle-to-everything (C-V2X) communication is critical for Intelligent Transportation Systems (ITS), facilitating information exchange among road users and infrastructure. Since its first introduction in rel-15 by 3GPP, 5G NR-V2X features have continued to evolve, aiming to support increasingly advanced V2X services. Addressing diverse service requirements, spectrum scarcity, dynamic vehicular environments, and radio interference necessitates efficient resource allocation strategies for the 5G NR-V2X system. However, dealing with resource allocation problems involving various conflicting objectives and constraints while accomplishing the Quality of Services (QoS) requirements of the V2X system remains a challenging issue. In this direction, this survey examines state-of-the-art resource allocation strategies for 5G NR-V2X, focusing on 3GPP features associated with V2X communication and their implications, along with optimization techniques employed in designing resource allocation strategies. Specifically, we present the benefits and challenges of each 3GPP feature and optimization technique, and their application to communication and computing resource allocation problems. Finally, we discuss issues tied to 3GPP features and optimization techniques, then highlight future research opportunities for efficient 5G NR-V2X resource allocation.
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institution DOAJ
issn 2687-7813
language English
publishDate 2025-01-01
publisher IEEE
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series IEEE Open Journal of Intelligent Transportation Systems
spelling doaj-art-8b7bf631e43542158c36ca9b3dd253292025-08-20T03:09:38ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132025-01-01696799410.1109/OJITS.2025.358402411054065Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A SurveyChaeriah Bin Ali Wael0https://orcid.org/0000-0002-9914-6530El Hadj Dogheche1Nasrullah Armi2Agus Subekti3Iyad Dayoub4https://orcid.org/0000-0003-0910-4722Institut d’électronique de microélectronique et de nanotechnologie, UMR CNRS 8520, Université Polytechnique Hauts-de-France, Valenciennes, FranceInstitut d’électronique de microélectronique et de nanotechnologie, UMR CNRS 8520, Université Polytechnique Hauts-de-France, Valenciennes, FranceResearch Center for Telecommunication, National Research and Innovation Agency (BRIN), Bandung, IndonesiaResearch Center for Telecommunication, National Research and Innovation Agency (BRIN), Bandung, IndonesiaInstitut d’électronique de microélectronique et de nanotechnologie, UMR CNRS 8520, Université Polytechnique Hauts-de-France, Valenciennes, FranceCellular Vehicle-to-everything (C-V2X) communication is critical for Intelligent Transportation Systems (ITS), facilitating information exchange among road users and infrastructure. Since its first introduction in rel-15 by 3GPP, 5G NR-V2X features have continued to evolve, aiming to support increasingly advanced V2X services. Addressing diverse service requirements, spectrum scarcity, dynamic vehicular environments, and radio interference necessitates efficient resource allocation strategies for the 5G NR-V2X system. However, dealing with resource allocation problems involving various conflicting objectives and constraints while accomplishing the Quality of Services (QoS) requirements of the V2X system remains a challenging issue. In this direction, this survey examines state-of-the-art resource allocation strategies for 5G NR-V2X, focusing on 3GPP features associated with V2X communication and their implications, along with optimization techniques employed in designing resource allocation strategies. Specifically, we present the benefits and challenges of each 3GPP feature and optimization technique, and their application to communication and computing resource allocation problems. Finally, we discuss issues tied to 3GPP features and optimization techniques, then highlight future research opportunities for efficient 5G NR-V2X resource allocation.https://ieeexplore.ieee.org/document/11054065/3GPP features5G NR-V2Xgame theoryoptimization techniquereinforcement learning (RL)resource allocation
spellingShingle Chaeriah Bin Ali Wael
El Hadj Dogheche
Nasrullah Armi
Agus Subekti
Iyad Dayoub
Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey
IEEE Open Journal of Intelligent Transportation Systems
3GPP features
5G NR-V2X
game theory
optimization technique
reinforcement learning (RL)
resource allocation
title Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey
title_full Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey
title_fullStr Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey
title_full_unstemmed Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey
title_short Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey
title_sort leveraging 3gpp features and optimization techniques for 5g nr v2x resource allocation a survey
topic 3GPP features
5G NR-V2X
game theory
optimization technique
reinforcement learning (RL)
resource allocation
url https://ieeexplore.ieee.org/document/11054065/
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