Energy Minimization for MEC-enabled Cellular-Connected UAV: Trajectory Optimization and Resource Scheduling

Recent years, under the cooperation of 5G techniques which enables high rate and low delay communication, cellular-connected unmanned aerial vehicles (UAVs) have attracted tremendous attentions due to its broad applications. In this paper, we focus on a mobile edge computing (MEC) enabled cellular-connected UAV system, where a UAV carrying a fixed amount of computation tasks is deployed to fly from an initial location to a final location and served by ground base stations (GBSs) in the presence of a ground eavesdropper. During the flight, the UAV can offload part of the tasks to GBSs for remote execution. We aim to minimize the total energy consumption of UAV to complete the task, including computation energy, communication energy and flight-propulsion energy, by jointly optimizing UAV trajectory, computation task allocation, UAV-GBS association and transmit power allocation. To tackle the non-convexity of the original problem, we resort to the block coordinate decent and successive convex approximation techniques through decomposing it into three sub-problems, which are alternately solved until the algorithm converges. Numerical results show that the proposed design effectively saves UAV's energy consumption for completing the given tasks.