Enhanced Velocity-Adaptive Scheme: Joint Fair Access and Age of Information Optimization in Vehicular Networks
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918103382753280 |
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| author | Xu, Xiao Wu, Qiong Fan, Pingyi Wang, Kezhi Cheng, Nan Chen, Wen Letaief, Khaled B. |
| author_facet | Xu, Xiao Wu, Qiong Fan, Pingyi Wang, Kezhi Cheng, Nan Chen, Wen Letaief, Khaled B. |
| contents | In this paper, we consider the fair access problem and the Age of Information (AoI) under 5G New Radio (NR) Vehicle-to-Infrastructure (V2I) Mode 2 in vehicular networks. Specifically, vehicles follow Mode 2 to communicate with Roadside Units (RSUs) to obtain accurate data for driving assistance.Nevertheless, vehicles often have different velocity when they are moving in adjacent lanes, leading to difference in RSU dwelltime and communication duration. This results in unfair access to network resources, potentially influencing driving safety. To ensure the freshness of received data, the AoI should be analyzed. Mode 2 introduces a novel preemption mechanism, necessitating simultaneous optimization of fair access and AoI to guarantee timely and relevant data delivery. We propose a joint optimization framework for vehicular network, defining a fairness index and employing Stochastic Hybrid Systems (SHS) to model AoI under preemption mechanism. By adaptively adjusting the selection window of Semi-Persistent Scheduling (SPS) in Mode 2, we address the optimization of fairness and AoI. We apply a large language model (LLM)-Based Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) to solve this problem. Simulation results demonstrate the effectiveness of our scheme in balancing fair access and minimizing AoI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18328 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Velocity-Adaptive Scheme: Joint Fair Access and Age of Information Optimization in Vehicular Networks Xu, Xiao Wu, Qiong Fan, Pingyi Wang, Kezhi Cheng, Nan Chen, Wen Letaief, Khaled B. Networking and Internet Architecture In this paper, we consider the fair access problem and the Age of Information (AoI) under 5G New Radio (NR) Vehicle-to-Infrastructure (V2I) Mode 2 in vehicular networks. Specifically, vehicles follow Mode 2 to communicate with Roadside Units (RSUs) to obtain accurate data for driving assistance.Nevertheless, vehicles often have different velocity when they are moving in adjacent lanes, leading to difference in RSU dwelltime and communication duration. This results in unfair access to network resources, potentially influencing driving safety. To ensure the freshness of received data, the AoI should be analyzed. Mode 2 introduces a novel preemption mechanism, necessitating simultaneous optimization of fair access and AoI to guarantee timely and relevant data delivery. We propose a joint optimization framework for vehicular network, defining a fairness index and employing Stochastic Hybrid Systems (SHS) to model AoI under preemption mechanism. By adaptively adjusting the selection window of Semi-Persistent Scheduling (SPS) in Mode 2, we address the optimization of fairness and AoI. We apply a large language model (LLM)-Based Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) to solve this problem. Simulation results demonstrate the effectiveness of our scheme in balancing fair access and minimizing AoI. |
| title | Enhanced Velocity-Adaptive Scheme: Joint Fair Access and Age of Information Optimization in Vehicular Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2507.18328 |