Task Offloading in UAV-to-Cell MEC Networks: Cell Clustering and Path Planning

When a natural disaster occurs, ground base stations (BSs) are destroyed and cannot provide communication services. Rapid restoration of communication is of great significance to the lives of trapped persons. This paper studies the problem of unmanned aerial vehicle (UAV) equipped with mobile edge computing (MEC) servers to provide communication and computing services for ground users in the scenario where the ground infrastructure is destroyed. We designed a UAV-to-Cell offloading system, which provides services in units of cells. By determining the hover locations (HLs) and trajectories, the UAV can handle more tasks with limited battery energy. Since tasks have time limit requirements, the order of processing will affect the task data size of the system. We solve this problem by joint cell clustering and path planning. Among them, elliptic clustering is used to divide the cells, the 3D position of the UAV is determined according to the quality of user service, and the double deep Q-network (DDQN) algorithm is used to determine the trajectory of the UAV. Simulation experiments demonstrate the effectiveness and efficiency of our proposed strategy by comparing it with the baselines.