A Deep Learning-Based Approach to Resource Allocation in UAV-aided Wireless Powered MEC Networks

Beamforming and non-orthogonal multiple access (NOMA) are two key techniques for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is mounted with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. We aim to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The considered optimization problem is non-convex involving joint optimization of the UAV's 3D placement and hybrid beamforming matrices as well as computation resource allocation in partial offloading pattern, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we propose an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. A resource allocation algorithm for partial offloading pattern is thereby proposed. Simulation results demonstrate that our designed algorithm yields a significant computation performance enhancement as compared to the benchmark schemes.