无人机辅助多MEC服务器的任务完成率最大化方案

Task completion maximization scheme for UAV-assisted multi-MEC servers

针对传统移动边缘计算(Mobile Edge Computing,MEC)网络中的严重信道衰落以及单一MEC服务器计算资源有限的问题,提出一种无人机(Unmanned Aerial Vehicles,UAV)辅助多MEC服务器的任务完成率最大化方案。该方案联合优化任务卸载决策、计算和通信资源及UAV轨迹,在满足信息因果关系约束、任务约束和轨迹约束的前提下,建立任务完成率最大化和UAV与用户加权能耗最小化问题。为求解该问题,采用交替优化算法将高度复杂问题解耦为任务卸载问题、资源分配问题和UAV轨迹设计问题,利用变量替换与拉格朗日对偶算法对任务卸载和资源分配问题转换后的凸问题进行迭代求解,并通过连续凸近似算法优化UAV轨迹。仿真结果表明,所提方案可以有效提高任务完成率并降低系统能耗,能够有效缓解信道衰落以及单一MEC服务器计算资源有限的问题。

The problems of severe channel fading and limited computation capacity of a single MEC server in a traditional mobile edge computing(MEC)network are investigated.The task completion maximization scheme is proposed in an unmanned aerial vehicles(UAV)assisted multi-MEC server network.The scheme jointly optimizes task offloading decision,computation and communication resource,and UAV trajectory to formulate the task completion rate maximization and the weighted energy consumption minimization problem between UAV and user,while satisfying the information causality constraint,task constraint,and trajectory constraint.In order to solve this problem,an alternating optimization algorithm is adopted to decouple the highly complex problem into a task offloading and resource allocation problem,as well as a UAV trajectory design problem.For the task offloading and resource allocation problems,the variable substitution algorithm and the Lagrangian dual algorithm are used to solve the transformed convex problems iteratively.The UAV trajectory is optimized with the successive convex approximation algorithm.Simulation results show that the proposed scheme can effectively improve the task completion rate and reduce the system energy consumption,and can effectively alleviate the problems of channel fading and limited computational capacity of a single MEC server.