Joint Service Caching and Trajectory Optimization for Multi-UAV Assisted Multi-access Edge Computing

Unmanned aerial vehicles (UAVs) play a pivotal role in augmenting multi-access edge computing by facilitating low-latency services for ground units (GUs), especially in areas where the ground infrastructure is inadequate or damaged. In this context, the UAV trajectory planning and caching strategies assume paramount importance to ensure low-latency service delivery. Due to the limited caching and computing resources at a single UAV, it alone cannot provide effective services for a large number of GUs. In this paper, we design a novel cooperative framework for low-latency service provisioning by coordinating multiple UAVs's trajectories and service caching strategies. We formulate a latency minimization problem to jointly optimize both service caching and trajectory planning of multiple UAVs. However, due to the high dimension and coupling of multiple UAVs' movement and service caching, the optimization problem is a mixed-integer nonlinear programming, which is typically an NP-hard problem, and we propose an effective algorithm based on deep deterministic policy gradient to solve the high dimensional, non-convex, and continuous long-term optimization problem. Numerous experiments confirm that the proposed algorithm achieves significantly better performance in reducing the total system delay than other baseline algorithms.