  In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge
computing (MEC) with the objective to optimize computation offloading with
minimum UAV energy consumption. In the considered scenario, a UAV plays the
role of an aerial cloudlet to collect and process the computation tasks
offloaded by ground users. Given the service requirements of users, we aim to
maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the
user transmit power, and computation load allocation. The resulting
optimization problem corresponds to nonconvex fractional programming, and the
Dinkelbach algorithm and the successive convex approximation (SCA) technique
are adopted to solve it. Furthermore, we decompose the problem into multiple
subproblems for distributed and parallel problem solving. To cope with the case
when the knowledge of user mobility is limited, we adopt a spatial distribution
estimation technique to predict the location of ground users so that the
proposed approach can still be applied. Simulation results demonstrate the
effectiveness of the proposed approach for maximizing the energy efficiency of
UAV.
