Velocity and Density-Aware RRI Analysis and Optimization for AoI Minimization in IoV SPS
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914085853986816 |
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| author | Ji, Maoxin Wang, Tong Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen |
| author_facet | Ji, Maoxin Wang, Tong Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen |
| contents | Addressing the problem of Age of Information (AoI) deterioration caused by packet collisions and vehicle speed-related channel uncertainties in Semi-Persistent Scheduling (SPS) for the Internet of Vehicles (IoV), this letter proposes an optimization approach based on Large Language Models (LLM) and Deep Deterministic Policy Gradient (DDPG). First, an AoI calculation model influenced by vehicle speed, vehicle density, and Resource Reservation Interval (RRI) is established, followed by the design of a dual-path optimization scheme. The DDPG is guided by the state space and reward function, while the LLM leverages contextual learning to generate optimal parameter configurations. Experimental results demonstrate that LLM can significantly reduce AoI after accumulating a small number of exemplars without requiring model training, whereas the DDPG method achieves more stable performance after training. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_08911 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Velocity and Density-Aware RRI Analysis and Optimization for AoI Minimization in IoV SPS Ji, Maoxin Wang, Tong Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Machine Learning Networking and Internet Architecture Addressing the problem of Age of Information (AoI) deterioration caused by packet collisions and vehicle speed-related channel uncertainties in Semi-Persistent Scheduling (SPS) for the Internet of Vehicles (IoV), this letter proposes an optimization approach based on Large Language Models (LLM) and Deep Deterministic Policy Gradient (DDPG). First, an AoI calculation model influenced by vehicle speed, vehicle density, and Resource Reservation Interval (RRI) is established, followed by the design of a dual-path optimization scheme. The DDPG is guided by the state space and reward function, while the LLM leverages contextual learning to generate optimal parameter configurations. Experimental results demonstrate that LLM can significantly reduce AoI after accumulating a small number of exemplars without requiring model training, whereas the DDPG method achieves more stable performance after training. |
| title | Velocity and Density-Aware RRI Analysis and Optimization for AoI Minimization in IoV SPS |
| topic | Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2510.08911 |