UAV-Assisted Resource Allocation Strategy in Energy Harvesting Edge Computing System

Aiming at the problem that the computing tasks requested by user equipments of (UEs) exceed the computing capacity of mobile edge computing (MEC) server in the ground base station (BS), an unmanned aerial vehicle (UAV)-assisted resource allocation strategy is proposed in this paper. By deploying a UAV carried with an MEC server, when the computing tasks requested by the UEs are beyond the computing capacity of the ground BS MEC server, the UEs can offload the extra computing tasks to the UAV. The resource allocation problem is formulated as a nonlinear programming problem by jointly optimizing transmitting power, system bandwidth, and computing resources. The objective is to minimize system energy consumption while satisfying the constraints of energy consumption, computing resource, and transmitting power. Genetic algorithm (GA) and nonlinear programming methods are combined to obtain the optimal solution to the formulated optimization problem. Simulation results demonstrate that the system energy consumption can be reduced to some extent under our proposed approach compared with the traditional GA and the partial fixed power, system bandwidth, or computing resources method based on GA and nonlinear programming.