Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I Networks
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Abstract (arXiv)
Vehicle-to-Infrastructure (V2I) technology enables information exchange between vehicles and road infrastructure. Specifically, when a vehicle approaches a roadside unit (RSU), it can exchange information with the RSU to obtain accurate data that assists in driving. With the release of the 3rd Generation Partnership Project (3GPP) Release 16, which includes the 5G New Radio (NR) Vehicle-to-Everything (V2X) standards, vehicles typically adopt mode-2 communication using sensing-based semi-persistent scheduling (SPS) for resource allocation. In this approach, vehicles identify candidate resources within a selection window and exclude ineligible resources based on information from a sensing window. However, vehicles often drive at different speeds, resulting in varying amounts of data transmission with RSUs as they pass by, which leads to unfair access. Therefore, it is essential to design an access scheme that accounts for different vehicle speeds to achieve fair access across the network. This paper formulates an optimization problem for vehicular networks and proposes a multi-objective optimization scheme to address it by adjusting the selection window in the SPS mechanism of 5G NR V2I mode-2. Simulation results demonstrate the effectiveness of the proposed scheme
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References · 18
- 2021A Tutorial on 5G NR V2X Communicationscited by 29
- 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
- 2022High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networkscited by 26
- 2023Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoonscited by 23
- 2023URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computingcited by 22
- 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communicationscited by 18
- 2024Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Networkcited by 17
- 2024Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learningcited by 17
- 2024Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNetscited by 17
- 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
- 2024Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networkscited by 12
- 2024Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learningcited by 11
6 more
- 2024Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learningcited by 11
- 2024Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systemscited by 9
- 2024Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocationcited by 5
- 2021Velocity-adaptive Access Scheme for MEC-assisted Platooning Networks: Access Fairness Via Data Freshnesscited by 4
- 2021Adaptive Semi-Persistent Scheduling for Enhanced On-road Safety in Decentralized V2X Networkscited by 3
- 2018Reducing Message Collisions in Sensing-based Semi-Persistent Scheduling (SPS) by Using Reselection Lookaheads in Cellular V2Xcited by 1
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