面向空天地异构网络的边缘计算部分任务卸载策略

李斌; 刘文帅; 费泽松

doi:10.11999/JEIT220272

面向空天地异构网络的边缘计算部分任务卸载策略

doi: 10.11999/JEIT220272

1.
南京信息工程大学计算机与软件学院南京 210044
2.
北京理工大学信息与电子学院北京 100081

基金项目: 科技部重点研发项目(2020YFB1806900)，国家自然科学基金(62101277)，江苏省自然科学基金(BK20200822)，江苏省研究生科研与实践创新计划项目(KYCX22_1204)

详细信息

作者简介:
李斌：男，副教授，硕士生导师，研究方向为移动边缘计算、无人机通信网络

刘文帅：男，硕士生，研究方向为移动边缘计算

费泽松：男，教授，博士生导师，研究方向为空天地一体化网络、无线多媒体通信、信号处理

通讯作者:
李斌　bin.li@nuist.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2022-03-14
- 修回日期: 2022-05-28
- 网络出版日期: 2022-06-14
- 刊出日期: 2022-09-19

Partial Computation Offloading for Mobile Edge Computing in Space-Air-Ground Integrated Network

1.
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds: The National Key R&D Program of China (2020YFB1806900), The National Natural Science Foundation of China (62101277), The Science Foundation of Jiangsu Province (BK20200822), The Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_1204)

摘要

摘要: 空天地异构网络作为一种新型网络构架，是未来6G实现泛在连接的关键支撑。该文提出一种面向空天地异构网络(SAGIN)的移动边缘计算部分任务卸载方案。首先，分析了低轨(LEO)卫星的覆盖时间。其次，联合考虑用户与无人机(UAV)匹配关联因子、任务分配、带宽分配、无人机计算资源分配以及无人机轨迹，旨在建立一个能耗最小化问题。最后，采用交替迭代优化算法，将原非凸问题分解为3个子问题，并利用变量替换和连续凸逼近方法将问题转化为凸问题进行求解。仿真结果表明，所提算法具有良好的收敛性能，并有效地降低系统能耗。
- 空天地一体化网络 /
- 移动边缘计算 /
- 计算卸载 /
- 无人机
Abstract: As a new type of network architecture, Space-Air-Ground Integrated Network (SAGIN) is the key support for 6G to realize ubiquitous connection in the future. In this paper, a partial task offloading approach for Mobile Edge Computing (MEC) in SAGIN is proposed. Firstly, the coverage time of Low Earth Orbit (LEO) satellite is analyzed. Then, a non-convex and multivariable coupling problem for minimization of total energy consumption of all Unmanned Aerial Vehicles (UAVs) is formulated by the joint design of the association control, computation task allocation, bandwidth allocation, UAV computation resource, and UAV trajectory. To solve this problem, the alternation optimization technique is invoked to decouple the original non-convex problem into three subproblems which are solved by the successive convex approximation method. Numerical results demonstrate that the proposed algorithm has good convergence performance and reduces effectively the system energy consumption.
- Space-Air-Ground Integrated Network (SAGIN) /
- Mobile Edge Computing (MEC) /
- Computation offloading /
- Unmanned Aerial Vehicles (UAVs)

HTML全文

图 1 系统模型图

下载: 全尺寸图片幻灯片

图 2 低轨卫星与地面终端用户的几何关系图

下载: 全尺寸图片幻灯片

图 3 K=12时多个UAV的飞行轨迹

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图 4 交替优化算法的收敛曲线

下载: 全尺寸图片幻灯片

图 5 K=5时用户在不同时隙内的卸载速率

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图 6 UAV能耗随用户数量变化

下载: 全尺寸图片幻灯片

表 1 交替优化算法(算法1)

输入：初始化$ \left( {{\boldsymbol{a}},{\boldsymbol{b}},{\boldsymbol{D}},{\boldsymbol{f}},{\boldsymbol{q}}} \right) $
for $i = 1:I$
步骤1：固定$\left\{ {{\boldsymbol{b}},{\boldsymbol{D}},{\boldsymbol{f}},{\boldsymbol{q}}} \right\}$时，求解线性规划问题；
步骤2：固定$ \left\{ {{\boldsymbol{a}},{\boldsymbol{f}},{\boldsymbol{q}}} \right\} $时，求解线性规划问题(10)；
步骤3：固定$ \left\{ {{\boldsymbol{a}},{\boldsymbol{b}},{\boldsymbol{D}}} \right\} $时，求解凸优化问题；
输出$ \left( {{\boldsymbol{a}},{\boldsymbol{b}},{\boldsymbol{D}},{\boldsymbol{f}},{\boldsymbol{q}}} \right) $。

下载: 导出CSV

参考文献(18)

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