用大模型优化车队边缘缓存，降低内容访问延迟。
LLM-Empowered Cooperative Content Caching in Vehicular Fog Caching-Assisted Platoon Networks
- 用大模型分析用户、内容和系统状态，智能决策缓存位置。
- 三层次缓存架构使内容获取延迟显著降低。
- 适合车联网、智能交通等需要低时延的场景。
本文提出一种面向车辆雾缓存（VFC）辅助车队网络的新型三层内容缓存架构，其中由靠近车队行驶的车辆构成VFC。系统通过协调本地车队车辆、动态VFC集群与云服务器（CS）的存储资源，以最小化内容获取延迟。为高效管理分布式存储，引入大语言模型（LLMs）实现实时智能缓存决策。该方法利用LLMs处理异构信息的能力，包括用户画像、历史数据、内容特征及动态系统状态。通过设计的提示框架编码任务目标与缓存约束，将缓存问题建模为决策任务；结合分层确定性缓存映射策略，实现对请求的自适应预测与三层次间的精确内容放置，无需频繁重训练。仿真结果验证了所提方案的优势。
原文摘要 · Abstract (English)
This letter proposes a novel three-tier content caching architecture for Vehicular Fog Caching (VFC)-assisted platoon, where the VFC is formed by the vehicles driving near the platoon. The system strategically coordinates storage across local platoon vehicles, dynamic VFC clusters, and cloud server (CS) to minimize content retrieval latency. To efficiently manage distributed storage, we integrate large language models (LLMs) for real-time and intelligent caching decisions. The proposed approach leverages LLMs' ability to process heterogeneous information, including user profiles, historical data, content characteristics, and dynamic system states. Through a designed prompting framework encoding task objectives and caching constraints, the LLMs formulate caching as a decision-making task, and our hierarchical deterministic caching mapping strategy enables adaptive requests prediction and precise content placement across three tiers without frequent retraining. Simulation results demonstrate the advantages of our proposed caching scheme.
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