Mobile-Edge Computing in the Sky: Energy Optimization for Air-Ground Integrated Networks

Unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future wireless networks to provide extensive coverage and additional computational capabilities for user equipments (UEs). In this article, we study mobile-edge computing (MEC) in air-ground integrated wireless networks, including ground computational access points (GCAPs), UAVs, and UEs, where UAVs and GCAPs cooperatively provide computing resources for UEs. Our goal is to minimize the total energy consumption of UEs by jointly optimizing users' association, uplink power control, channel allocation, computation capacity allocation, and UAV 3-D placement, subject to the constraints on deterministic binary offloading, UEs' latency requirements, computation capacity, UAV power consumption, and available bandwidth. Due to the nonconvexity of the primary problem and the coupling of variables, we introduce a coordinate descent algorithm that decomposes the UEs' energy consumption minimization problem into several subproblems which can be efficiently solved. The simulation results demonstrate the advantages of the proposed algorithm in terms of the reduced total energy consumption of UEs.