基于反向散射无人机辅助MEC网络能耗最小化方案  

Energy consumption minimization scheme for UAV-assisted MEC network based on backscatter

作　　者：刘伯阳 党儒鸽 王丽平 张浩然 刘超文 LIU Boyang;DANG Ruge;WANG Liping;ZHANG Haoran;LIU Chaowen(School of Communications and Information Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China;Shaanxi Key Laboratory of Information Communication Network and Security,Xi’an 710121;ASN UAS Flight Test and Research Jingbian Co.,Ltd,Yulin 718500,China)

机构地区：[1]西安邮电大学通信与信息工程学院,陕西西安710121 [2]陕西省信息通信网络及安全重点实验室,陕西西安710121 [3]爱生无人机试验测试靖边有限公司,陕西榆林718500

出　　处：《西安邮电大学学报》2024年第6期1-10,共10页Journal of Xi’an University of Posts and Telecommunications

基　　金：国家自然科学基金项目(61901366);陕西省自然科学基础研究计划项目(2020JQ-851);陕西省普通高校青年杰出人才支持计划项目。

摘　　要：针对移动边缘计算(Mobile Edge Computing,MEC)网络中由于用户设备电池容量和任务处理能力有限导致的服务性能受限的问题,提出一种基于反向散射(Backscatter Communication,BackCom)无人机(Unmanned Aerial Vehicle,UAV)辅助MEC网络能耗最小化方案。该方案采用BackCom和主动传输混合卸载技术,建立一个包括计算资源调度和UAV轨迹的非凸优化问题,结合块坐标下降法和连续凸近似算法获取原问题的最优解。仿真结果表明,所提方案在最大限度降低能耗的同时,有效地降低了数据处理延迟,且在不同参数下均有效。For the issue of limited service performance in mobile edge computing(MEC)networks caused by the constraints of user device battery capacity and task processing capability,a solution for energy minimization in MEC networks is proposed,which adopts unmanned aerial vehicles(UAVs)based on backscatter communication(BackCom).A non-convex optimization problem including computational resource scheduling and UAV trajectory is established by using a hybrid offload technique of BackCom and active transport.The optimal solution of the original problem is obtained by combining block coordinate descent method and continuous convex approximation technique.Simulation results show that the proposed scheme can effectively reduce the data processing delay while minimizing the energy consumption,and it is effective under different parameters.

关 键 词：移动边缘计算 无人机 反向散射通信 主动传输 块坐标下降法 连续凸近似算法 

分 类 号：TN929.5[电子电信—通信与信息系统]