基于自供电无人机远距离中继通信与计算卸载策略优化研究  

Study on Optimization of Long-distance Relay Communication and Computational Offloading Strategy Based on Self-powered UAVs

作　　者：薛建彬[1] 田桂英 马玉玲 邵斐 王涛 XUE Jianbin;TIAN Guiying;MA Yuling;SHAO Fei;WANG Tao(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China)

机构地区：[1]兰州理工大学计算机与通信学院,兰州730050

出　　处：《计算机科学》2024年第S02期720-726,共7页Computer Science

基　　金：甘肃省创新基金项目(2022A-215);甘肃省科技计划基金项目(23YFGA0062)。

摘　　要：移动边缘计算(MEC)在无线用户服务中发挥了重要作用,显著提升了计算服务的效率。然而,随着地面用户数量的快速增长,无线设备直接访问MEC节点变得日益困难。针对这一挑战,提出了一种创新的通信系统模型,该模型利用自充电无人机(UAV)协同地面基站(包括MEC节点和能量发射站LS),旨在提升地面无线通信系统的性能。深入探讨了UAV-MEC系统、能量发射站(LS)、IoT设备以及边缘云(EC)之间的协同工作机制。综合考虑了UAV的功耗、LS对UAV的充电过程以及RF-DC信号的转换损耗,目标是在保证UAV持续稳定运行的同时,最大化其完成任务后的剩余能量。其次,联合优化了UAV的悬停位置、通信和计算资源的分配以及任务分割的决策,旨在最小化UAV的能耗,同时确保无线通信系统整体性能的最优化。由于该问题高度非凸,提出了一种基于逐次凸逼近的高效算法,以获取次优解。通过大量的仿真实验,验证了所提方案在实际应用中的性能,其显著优于基准方案。Mobile edge computing(MEC)plays an important role in wireless subscriber services and significantly improves the efficiency of computing services.However,with the rapid growth of the number of terrestrial users,it becomes increasingly difficult for wireless devices to directly access MEC nodes.To address this challenge,this paper proposes an innovative communication system model that utilizes self-charging unmanned aerial vehicles(UAVs)to collaborate with terrestrial base stations,including MEC nodes and energy transmitting station LSs,aiming to enhance the performance of terrestrial wireless communication systems.The cooperative working mechanism between UAV-MEC system,energy launching station(LS),IoT devices,and edge cloud(EC)is deeply explored.The power consumption of the UAV,the charging process of the LS to the UAV,and the conversion loss of the RF-DC signals are considered comprehensively,aiming to maximize the residual energy of the UAV after completing its mission while ensure its continuous and stable operation.Secondly,the UAV’s hovering position,the allocation of communication and computational resources,and the decision of task segmentation are jointly optimized with the aim of minimizing the UAV’s energy consumption while ensuring the optimization of the overall performance of the wireless communication system.Since the problem is highly nonconvex,an efficient algorithm based on successive convex approximation is proposed to obtain a suboptimal solution.Extensive simulation experiments verify that the proposed scheme significantly outperforms the baseline schemes in practical applications.

关 键 词：无人机 移动边缘计算 无线电力传输 计算卸载能耗 资源分配 

分 类 号：TN929[电子电信—通信与信息系统]