Resource Allocation in UAV-Enabled Wireless-Powered MEC Networks With Hybrid Passive and Active Communications

This article proposes a novel unmanned aerial vehicle (UAV)-enabled wireless-powered mobile edge computing (WP-MEC) network, where several Internet of Things (IoT) nodes use the energy harvested from the UAV's radio frequency signals to support the local computation and the hybrid active-passive communications-based task offloading. Two weighted sum computation bits (WSCB) maximization problems are formulated under the partial and binary offloading, respectively, by jointly optimizing the local computing frequencies and time, the IoT nodes' reflection coefficients, the IoT nodes' transmit powers, the UAV's trajectory, etc., subject to the quality-of-service and energy-causality constraints per IoT node, the speed constraint of the UAV, etc. Since the formulated problems are highly nonconvex, two iterative algorithms are proposed to solve the formulated problems under two modes. Simulation results demonstrate that the proposed iterative algorithms have a fast convergence rate, and the proposed schemes achieve higher WSCB than several baseline schemes.