用大模型与强化学习优化车联网中信息时效性，提升数据更新效率。
Velocity and Density-Aware RRI Analysis and Optimization for AoI Minimization in IoV SPS
- 结合车速、车流密度与资源预留间隔，构建时延感知的时效性模型
- 小样本下大模型可快速降低平均信息年龄，强化学习训练后更稳定
- 适合车联网实时调度优化，尤其适用于动态交通场景
针对车联网半持久调度中因包碰撞和车速相关的信道不确定性导致的信息时效性（AoI）恶化问题，本文提出一种基于大语言模型（LLM）与深度确定性策略梯度（DDPG）的优化方法。首先建立受车辆速度、车流密度和资源预留间隔（RRI）影响的AoI计算模型，随后设计双路径优化方案：DDPG在状态空间与奖励函数引导下进行策略学习，而LLM通过上下文学习生成最优参数配置。实验表明，仅需少量示例，LLM即可显著降低AoI且无需模型训练；而DDPG在训练后表现出更稳定的性能。该方法有效缓解了动态环境中信息更新延迟问题。
原文摘要 · Abstract (English)
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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