Adaptive task offloading of rechargeable UAV edge computing network based on double decision value iteration

Abstract

With the development of Internet of Things technology, unmanned aerial vehicles (UAVs) have attracted tremendous attentions due to its broad applications in the conditions of high rate and low delay communication. In this paper, we consider a wireless-powered and UAV-enabled MEC system. Because of the limitation of UAV’s battery capacity, we deploy microwave power stations next to the base stations to provide microwave energy to the UAV from ground microwave antenna array. The UAV receives tasks from mobile users and processes the task locally or offloads task to edge servers for processing, or charges its battery through microwave stations when its battery is low. Due to the uncertainty of the UAV’s trajectory and the working status of the microwave power stations, we build the offloading decision process as a finite Markov Decision Process(MDP) model. In the MDP model, we solve the optimization problem of minimizing the total consumption of time and energy through an optimal offloading algorithm. The algorithm we proposed is based on the fast-converging value iteration algorithm. Furthermore, extensive numerical results shows that the proposed algorithm outperforms other four conventional baseline schemes.