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

arXiv: https://arxiv.org/abs/2510.08911 · HTML: https://mlsanity.com/paper/2510.08911/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets](https://mlsanity.com/paper/2406.07996/) (2024)
- [DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV](https://mlsanity.com/paper/2411.13104/) (2024)
- [Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation](https://mlsanity.com/paper/2407.13123/) (2024)
- [Adaptive Semi-Persistent Scheduling for Enhanced On-road Safety in Decentralized V2X Networks](https://mlsanity.com/paper/2104.01804/) (2021)

## Abstract (arXiv)

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.