智能反射面赋能无人机边缘网络计算卸载方案

李斌; 刘文帅; 谢万城; 费泽松

doi:10.11959/j.issn.1000-436x.2022196

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智能反射面赋能无人机边缘网络计算卸载方案

学术通信 | 更新时间：2024-06-05

- 智能反射面赋能无人机边缘网络计算卸载方案
- Computation offloading scheme for RIS-empowered UAV edge network
- 通信学报 2022年43卷第10期页码：223-233
- 作者机构：
  
  1. 南京信息工程大学计算机与软件学院，江苏南京 210044
  2. 北京理工大学信息与电子学院，北京 100081
- 作者简介：
  
  [ "李斌（1987− ），男，山东济宁人，博士，南京信息工程大学副教授、硕士生导师，主要研究方向为无人机通信、移动边缘计算等" ]
  [ "刘文帅（1996− ），男，河北保定人，南京信息工程大学硕士生，主要研究方向为移动边缘计算、智能反射面等" ]
  [ "谢万城（2001− ），男，湖北宜昌人，南京信息工程大学硕士生，主要研究方向为移动边缘计算、智能反射面等" ]
  [ "费泽松（1977− ），男，安徽合肥人，博士，北京理工大学教授、博士生导师，主要研究方向为无线通信、多媒体信号处理等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2020YFB1806900);国家自然科学基金资助项目(62101277);江苏省自然科学基金资助项目(BK20200822);江苏省研究生科研与实践创新计划基金资助项目(KYCX22_1204)
- DOI：10.11959/j.issn.1000-436x.2022196
  中图分类号： TN92
- 网络出版日期：2022-10，
  
  纸质出版日期：2022-10-25
- 稿件说明：
移动端阅览
李斌, 刘文帅, 谢万城, 等. 智能反射面赋能无人机边缘网络计算卸载方案[J]. 通信学报, 2022,43(10):223-233.

Bin LI, Wenshuai LIU, Wancheng XIE, et al. Computation offloading scheme for RIS-empowered UAV edge network[J]. Journal on communications, 2022, 43(10): 223-233.
李斌, 刘文帅, 谢万城, 等. 智能反射面赋能无人机边缘网络计算卸载方案[J]. 通信学报, 2022,43(10):223-233. DOI： 10.11959/j.issn.1000-436x.2022196.

Bin LI, Wenshuai LIU, Wancheng XIE, et al. Computation offloading scheme for RIS-empowered UAV edge network[J]. Journal on communications, 2022, 43(10): 223-233. DOI： 10.11959/j.issn.1000-436x.2022196.

摘要

摘要：针对无人机辅助边缘计算城市场景，因无人机与地面用户之间的任务卸载链路易受障碍物遮挡而造成卸载速率低的问题，提出了一种智能反射面赋能的无人机边缘计算部分任务卸载方案。联合考虑任务分配、用户发射功率、智能反射面相移矩阵、无人机计算资源分配以及无人机轨迹，建立了一个用户最小平均数据吞吐量最大化问题。由于该问题是一个随机优化问题，且优化变量之间密切耦合，难以直接求解。因此，首先通过利用数学期望的性质，将随机优化问题转换为确定性优化问题；其次，利用块坐标下降算法将确定性优化问题分解为3个子问题，并通过引入辅助变量、利用连续凸近似和半定松弛技术将非凸问题转换为凸优化问题，进而得到问题的近似次优解。仿真结果表明，所提方案具有良好的收敛性能，并有效地提高了地面用户的平均数据吞吐量。

Abstract

In order to address the challenge of low offloading rate caused by the obstacles blocking in the links between unmanned aerial vehicle (UAV) and ground users (GU) in urban scene

a partial task offloading scheme for UAV-enabled mobile edge computing with the aid of reconfigurable intelligence surface was proposed.A nonconvex and multivariable coupling stochastic optimization problem was formulated by the joint design of the computation task allocation

the transmit power of GU

the phase shift of RIS

UAV computation resource

and UAV trajectory

aiming at maximizing the minimum average data throughput of GU.By leveraging the properties of mathematical expectation

the stochastic optimization problem was transformed into a deterministic optimization problem.Then

the deterministic optimization problem was decomposed into three subproblems by using the block coordinate descent (BCD) algorithm.By introducing auxiliary variables

the nonconvex problems were transformed into convex optimization problems via the successive convex approximation and semidefinite relaxation

and then the approximate suboptimal solution of the original problem was obtained.The simulation results show that the proposed algorithm has good convergence performance and effectively improves the average data throughput of GU.

关键词

Keywords

references

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