多UAV辅助反向散射边缘计算网络能耗优化方案
Power consumption optimization scheme for multi-UAV-assisted backscattering edge computing network
刘超文 1王丽平 2党儒鸽 2张浩然 2刘伯阳1
作者信息
- 1. 西安邮电大学 通信与信息工程学院,陕西 西安 710121;陕西省信息通信网络及安全重点实验室,陕西 西安 710121
- 2. 西安邮电大学 通信与信息工程学院,陕西 西安 710121
- 折叠
摘要
为了改善通信过程中由于遮挡导致信号衰落及设备能量不足以支撑大数据传输的问题,提出一种多无人机(Unmanned Aerial Vehicle,UAV)辅助反向散射边缘计算(Mobile Edge Computing,MEC)网络能耗优化方案.该方案根据 UAV与反向散射辅助 MEC系统模型构建非凸优化问题,利用块坐标下降法(Block Coodinate Descent,BCD)和变量替换将非凸优化问题转化为凸问题,再利用拉格朗日对偶算法和次梯度算法对该凸问题进行迭代求解.基于连续凸近似(Successive Convex Approximation,SCA)算法优化 UAV 轨迹,以最小化用户设备与 UAV 的能耗加权和.仿真结果表明,所提方案在不同参数下均有效可靠,与固定所有 UAV 轨迹、固定通信 UAV 轨迹、固定功率UAV轨迹等 3 种方案相比,所提方案能够有效降低系统能耗.
Abstract
In order to improve the signal degradation due to occlusion and insufficient device energy,so as to support large data transmission in the communication process,a power optimisation scheme is proposed for a multi-UAV assisted backscattered edge computing(MEC)network.The scheme constructs a non-convex optimisation problem based on the UAV and backscatter-assisted MEC sys-tem model,and then transforms the non-convex optimisation problem into a convex problem by u-sing the block coodinate descent(BCD)and variable substitution,and then solves the convex prob-lem iteratively by using the Lagrangian duality algorithm and the sub-gradient algorithm.The UAV trajectory is optimised based on the successive convex approximation(SCA)algorithm to minimise the weighted sum of the energy consumption of the user and the UAV.Simulation results show that the proposed scheme is effective and reliable under different parameters.Compared with the three schemes of fixed all UAV trajectories,fixed communication UAV trajectories and fixed power UAV trajectories,the proposed scheme can effectively reduce system energy consumption.
关键词
移动边缘计算/无人机/反向散射/块坐标下降法/拉格朗日对偶算法/次梯度算法/连续凸近似算法Key words
mobile edge computing/unmanned aerial vehicle/backscatter/block coodinate descent/Lagrangian duality algorithm/subgradient algorithm/successive convex approximation algorithm引用本文复制引用
基金项目
陕西省自然科学基础研究计划(2020JQ-851)
陕西省普通高等学校青年杰出人才支持计划()
出版年
2024
NETL
NSTL
维普
万方数据