Intelligent Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

Abstract

Unmanned aerial vehicles (UAVs) have been considered as effective flying base stations (FBSs) to provide on- demand wireless communications. Equipped with computation resource, UAVs are also capable of offering computation offloading opportunities for the mobile users (MUs) in mobile edge computing (MEC) networks. However, due to the small hardware and load capacity, UAVs can only supply limited computation and energy resource. It is thus challenging for UAVs to guarantee the quality of service (QoS) of MUs, while minimizing their total resource consumptions. Toward this end, instead of using all resource for every single task, we propose an intelligent resource allocation algorithm based on reinforcement learning, which enables UAVs to make energy-efficent and computation-efficent allocation decisions intelligently. Then, we take UAVs as learning agents by forming resource allocation decisions as actions and designing a reward function with the aim of minimizing the weighted resource consumptions. Each UAV performs the algorithm only based on its local observations without information exchange among different UAVs. Simulation results show that the proposed reinforcement learning based approach outperforms the benchmark algorithms in terms of weighted consumptions in a whole time period.