基于大语言模型的协同内容缓存用于车辆雾缓存辅助车列网络
LLM-Empowered Cooperative Content Caching in Vehicular Fog Caching-Assisted Platoon Networks
AI总结：
本文提出利用大语言模型实现车辆雾缓存辅助车列网络中的协同内容缓存，通过智能决策提升缓存效率与内容检索性能。
摘要：
本文提出了一种新颖的三级内容缓存架构，用于车辆雾缓存（VFC）辅助的车列系统，其中VFC由接近车列行驶的车辆组成。系统战略性地协调本地车列车辆、动态VFC集群和云服务器（CS）之间的存储，以最小化内容检索延迟。为了高效管理分布式存储，我们整合了大语言模型（LLMs）用于实时和智能的缓存决策。所提出的方法利用LLMs处理异构信息的能力，包括用户资料、历史数据、内容特征和动态系统状态。通过设计的提示框架编码任务目标和缓存约束，LLMs将缓存转化为决策任务，而我们的分层确定性缓存映射策略能够在无需频繁重新训练的情况下，实现适应性请求预测和精确内容放置，跨越三个层级。仿真结果展示了所提出缓存方案的优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
