Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Abstract

Recent years, unmanned aerial vehicles (UAVs) have attracted much attention for providing intermediate relay to ground mobile user equipments (UEs) for their flexible mobility. UEs can offload computing-intensive task to mobile cloud computing (MCC) or mobile edge computing (MEC) for fast processing. However, with multi-UAV and ground mobile UEs in the system, heterogeneous performance requirement as well as fast-changing communication condition make the system more complicated. Meanwhile, both UEs and UAVs are battery-driven. How to optimize the energy efficiency for UEs' transmission and UAVs' position should be carefully considered. Since this is a non-convex and mixed-integer optimization problem, a heuristic joint power and quality of experience (HJPQ) algorithm is proposed in this article, where the UEs' offloading delay, MIMO channel, transmission power, as well as UAVs' placement are jointly optimized. The numeral simulations not only reveal the effectiveness of HJPQ, but also guarantee the great quality of experience (QoE) performance for UEs with different priorities. Furthermore, the comparison experiments with random assignment and deep deterministic policy gradient (DDPG) show the superiority of HJPQ in lower complexity, faster convergence, shorter offloading delay as well as higher energy efficiency.