Joint optimization of trajectory and resource allocation in cellular-connected multi-UAV MEC networks

Abstract

Unmanned aerial vehicles (UAVs)-based mobile edge computing (MEC) has been introduced as a promising model for enhanced edge communications in the future of integrated air-sky-earth and sea communications. However, in UAV-assisted mobile edge computing systems, the energy consumption problem of UAVs during computational offloading becomes a serious challenge. Due to UAVs constrained energy and processing capabilities, the majority of current research does not adequately take into the task of offloading UAVs as end users and the energy consumption problem. Considering this, this paper proposes a novel cellular-connected multi-UAV MEC network in which the UAV can offload its computational tasks to a ground-based station (GBS) and the UAV can harvest energy from the GBS. Total UAV energy consumption is minimized by jointly optimizing the UAV-GBS association, UAV transmit power, UAV trajectory, and energy harvesting time. The formulated energy consumption minimization problem is a difficult non-convex optimization problem. To solve this issue, the optimization problem is first broken down into three sub-optimization problems and resolved using successive convex optimization techniques. Then a joint block coordinate descent (BCD) based resource allocation and UAV trajectory algorithm is proposed to solve the overall optimization problem. Finally, the effectiveness of the suggested scheme is verified by simulation. The simulation results demonstrate that the suggested strategy is more efficient and performs better.