李虎1,,,
陈善学1,
贺晓帆2
1.重庆邮电大学通信与信息工程学院 ??重庆 ??400065
2.美国德克萨斯州拉玛尔大学电子工程系 ??美国 ??77710
基金项目:国家自然科学基金(61771084, 61601071)，****和创新团队发展计划基金(IRT16R72)，重庆市基础研究与前沿探索项目(cstc2018jcyjAX0463)
详细信息
作者简介:张海波：男，1979年生，副教授，研究方向为无线资源管理
李虎：男，1992年生，硕士生，研究方向为移动边缘计算、无线资源管理
陈善学：男，1966年生，教授，研究方向为图像处理、数据压缩
贺晓帆：男，1985年生，助理教授，研究方向为无线资源优化
通讯作者:李虎 976502889@qq.com
中图分类号:TN929.5计量
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被引次数:0
出版历程
收稿日期:2018-06-13
修回日期:2019-01-21
网络出版日期:2019-02-14
刊出日期:2019-05-01
Computing Offloading and Resource Optimization in Ultra-dense Networks with Mobile Edge Computation
Haibo ZHANG1,Hu LI1,,,
Shanxue CHEN1,
Xiaofan HE2
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Department of Electronic Engineering, Lamar University, TX 77710, USA
Funds:The National Natural Science Foundation of China (61771084, 61601071), The Foundation for Changjiang Scholars and Innovative Research Team in University (IRT16R72), The Basic Research and Frontier Exploration Projects in Chongqing (cstc2018jcyjAX0463)
摘要
摘要:移动边缘计算(MEC)通过在无线网络边缘为用户提供计算能力，来提高用户的体验质量。然而，MEC的计算卸载仍面临着许多问题。该文针对超密集组网(UDN)的MEC场景下的计算卸载，考虑系统总能耗，提出卸载决策和资源分配的联合优化问题。首先采用坐标下降法制定了卸载决定的优化方案。同时，在满足用户时延约束下采用基于改进的匈牙利算法和贪婪算法来进行子信道分配。然后，将能耗最小化问题转化为功率最小化问题，并将其转化为一个凸优化问题得到用户最优的发送功率。仿真结果表明，所提出的卸载方案可以在满足用户不同时延的要求下最小化系统能耗，有效地提升了系统性能。
关键词:超密集组网/
移动边缘计算/
计算卸载/
资源分配
Abstract:Mobile Edge Computing (MEC) improves the quality of users experience by providing users with computing capabilities at the edge of the wireless network. However, computing offloading in MEC still faces some problems. In this paper, a joint optimization problem of offloading decision and resource allocation is proposed for the computation offloading problem in Ultra-Dense Networks (UDN) with MEC. To solve this problem, firstly, the coordinate descent method is used to formulate the optimization scheme for the offloading decision. Meanwhile, the improved Hungarian algorithm and greedy algorithm are used to allocate the channels to meet the user’s delay requirements. Finally, the problem of minimizing energy consumption is converted into a problem of minimizing power. Then it is converted into a convex optimization problem to get the user’s optimal transmission power. Simulation results show that the proposed scheme can minimize the energy consumption of the system while satisfying the users’ different delay requirements, and improve effectively the performance of the system.
Key words:Ultra-Dense Networks (UDN)/
Mobile Edge Computing (MEC)/
Computing offloading/
Resource allocation
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https://jeit.ac.cn/article/exportPdf?id=2994e198-8d4d-402c-a605-a03a69ebf3da
