LLM-Empowered Cooperative Content Caching in Vehicular Fog Caching-Assisted Platoon Networks

TL;DR

This paper introduces a three-tier content caching system for vehicular platoons that uses large language models to make real-time, intelligent caching decisions, reducing content retrieval latency in vehicular fog networks.

Contribution

It presents a novel LLM-based decision framework for hierarchical caching in vehicular fog networks, enhancing adaptive content placement without frequent retraining.

Findings

LLM-driven caching reduces latency significantly.

Hierarchical caching improves content hit rates.

Simulation confirms effectiveness of the proposed scheme.

Abstract

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.