  In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge
computing (MEC) architecture, in which a UAV roaming around the area may serve
as a computing server to help user equipment (UEs) compute their tasks or act
as a relay for further offloading their computation tasks to the access point
(AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs
subject to the task constraints, the information-causality constraints, the
bandwidth allocation constraints and the UAV's trajectory constraints. The
required optimization is nonconvex, and an alternating optimization algorithm
is proposed to jointly optimize the computation resource scheduling, bandwidth
allocation, and the UAV's trajectory in an iterative fashion. Numerical results
demonstrate that significant performance gain is obtained over conventional
methods. Also, the advantages of the proposed algorithm are more prominent when
handling computation-intensive latency-critical tasks.
