A Survey on Resource Allocation in Vehicular Networks
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Abstract (arXiv)
Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory Quality of Service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as erroneous and congested wireless channels (due to high mobility or uncoordinated channel-access), increasingly fragmented and congested spectrum, hardware imperfections, and anticipated growth of vehicular communication devices. Therefore, it will be critical to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation schemes for the two dominant vehicular network technologies, e.g. Dedicated Short Range Communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Sep 2019.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 4; 2025: 1.
9 papers.
- 2024Fair Resource Allocation in Multi-Task Learningcited by 34
- 2023Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviorscited by 25
- 2024Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learningcited by 17
- 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
- 2023Federated Reinforcement Learning for Resource Allocation in V2X Networkscited by 3
- 2024FedRSU: Federated Learning for Scene Flow Estimation on Roadside Unitscited by 2
- 2022An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networkscited by 0
- 2023Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Studycited by 0
- 2024Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communicationscited by 0
- 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey29 Apr 2025
- 2024Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning11 Jun 2024
- 2024Fair Resource Allocation in Multi-Task Learning23 Feb 2024
- 2024FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units23 Jan 2024
- 2024Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications4 Jan 2024
- 2023Federated Reinforcement Learning for Resource Allocation in V2X Networks15 Oct 2023
- 2023Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study4 Oct 2023
- 2023Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors12 May 2023
- 2022An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks26 Aug 2022