DRL-based Content Caching Strategy With Efficient User Preference Predictions in UAV-assisted VEC

In vehicular edge computing, Unmanned Aerial Vehicles (UAVs) have become a feasible solution for addressing high deployment costs faced by base stations in congested roads during peak hours. However, UAVs cannot cache all requested content due to limited storage. Hence, we propose a content caching strategy based on user preference predictions. To address resource consumption and user privacy concerns during the training process, we propose a user preference prediction model based on hierarchical federated learning training. Specifically, we employ a hierarchical clustering approach to partition user vehicles and UAVs into multiple clusters and utilize hierarchical federated learning to train prediction models within each cluster. Furthermore, to tackle the joint optimization problem of content caching and bandwidth allocation, we propose I-MADDPG, an improved multi-agent deep deterministic policy gradient algorithm. It determines the next continuous action based on the reward value at the current moment and the average reward value in the iteration period as reference parameters. The experimental results demonstrate that the proposed algorithm has significantly enhanced training efficiency compared to the baselines. Additionally, it has improved cache hit rate and reduced content request delay through effective resource allocation.