Velocity and Density-Aware RRI Analysis and Optimization for AoI Minimization in IoV SPS

• URL: http://arxiv.org/abs/2510.08911v1
• Date: Fri, 10 Oct 2025 01:50:23 GMT
• Title: Velocity and Density-Aware RRI Analysis and Optimization for AoI Minimization in IoV SPS
• Authors: Maoxin Ji, Tong Wang, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen,
• Abstract summary: This letter proposes an optimization approach based on Large Language Models (LLM) and Deep Deterministic Policy Gradient (DDPG)<n>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.<n> 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.
• Score: 30.378932790676384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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