  In this paper, we investigate a mobile edge computing (MEC) architecture with
the assistance of an unmanned aerial vehicle (UAV). The UAV acts as a computing
server to help the user equipment (UEs) compute their tasks as well as a relay
to further offload the UEs' tasks to the access point (AP) for computing. The
total energy consumption of the UAV and UEs is minimized by jointly optimizing
the task allocation, the bandwidth allocation and the UAV's trajectory, subject
to the task constraints, the information-causality constraints, the bandwidth
allocation constraints, and the UAV's trajectory constraints. The formulated
optimization problem is nonconvex, and we propose an alternating algorithm to
optimize the parameters iteratively. The effectiveness of the algorithm is
verified by the simulation results, where great performance gain is achieved in
comparison with some practical baselines, especially in handling the
computation-intensive and latency-critical tasks.
