无线供能边缘计算网络中系统计算能效最大化资源分配方案

施丽琴; 叶迎晖; 卢光跃

doi:10.11959/j.issn.1000-436x.2020182

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无线供能边缘计算网络中系统计算能效最大化资源分配方案

专题：面向万物互联的通信与计算融合 | 更新时间：2024-06-05

- 无线供能边缘计算网络中系统计算能效最大化资源分配方案
- Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network
- 通信学报 2020年41卷第10期页码：59-69
- 作者机构：
  
  西安邮电大学陕西省信息通信网络及安全重点实验室，陕西西安，710121
- 作者简介：
  
  [ "施丽琴（1993- ），女，安徽铜陵人，博士，西安邮电大学副教授，主要研究方向为边缘计算和无线供能网络" ]
  [ "叶迎晖（1991- ），男，浙江丽水人，博士，西安邮电大学副教授，主要研究方向为携能通信和边缘计算" ]
  [ "卢光跃（1971- ），男，河南南阳人，博士，西安邮电大学教授、博士生导师，主要研究方向为认知无线电、携能通信和边缘计算等" ]
- 基金信息：
  
  陕西省重点科技创新团队计划基金资助项目(2017KCT-30-02);国家自然科学基金资助项目(61901367);陕西省自然科学基金资助项目(2020JQ-844)
- DOI：10.11959/j.issn.1000-436x.2020182
  中图分类号： TN92
- 网络出版日期：2020-10，
  
  纸质出版日期：2020-10-25
- 稿件说明：
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施丽琴, 叶迎晖, 卢光跃. 无线供能边缘计算网络中系统计算能效最大化资源分配方案[J]. 通信学报, 2020,41(10):59-69.

Liqin SHI, Yinghui YE, Guangyue LU. Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network[J]. Journal on communications, 2020, 41(10): 59-69.
施丽琴, 叶迎晖, 卢光跃. 无线供能边缘计算网络中系统计算能效最大化资源分配方案[J]. 通信学报, 2020,41(10):59-69. DOI： 10.11959/j.issn.1000-436x.2020182.

Liqin SHI, Yinghui YE, Guangyue LU. Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network[J]. Journal on communications, 2020, 41(10): 59-69. DOI： 10.11959/j.issn.1000-436x.2020182.

摘要

针对无线供能边缘计算网络，提出了一种兼顾边缘服务器有限计算能力的系统计算能效最大化资源分配方法。具体而言，通过联合优化边缘服务器和用户的计算频率与时间、边缘用户的发射功率与卸载时间、能量收集时间、本地计算时间及专用能量站的发射功率来建立一个系统计算能效最大化的优化问题。由于所建立的问题是一个高度非凸的分式规划问题且难以求解，因此首先通过引入广义分式规划理论将原问题转化为一个减式非凸问题，然后利用一系列辅助变量将其转化为一个等价的凸问题，并据此提出一种迭代算法来获取原问题的最优解。仿真结果验证了所提迭代算法的快速收敛性，并通过与其他方案进行比较，证明了所提的资源分配方案能够取得更高的系统计算能效。

Abstract

For wireless powered mobile edge computing (MEC) network

a system computation energy efficiency (CEE) maximization scheme by considering the limited computation capacity at the MEC server side was proposed.Specifically

a CEE maximization optimization problem was formulated by jointly optimizing the computing frequencies and execution time of the MEC server and the edge user(EU)

the transmit power and offloading time of each EU

the energy harvesting time and the transmit power of the power beacon.Since the formulated optimization problem was a non-convex fractional optimization problem and hard to solve

the formulated problem was firstly transformed into a non-convex subtraction problem by means of the generalized fractional programming theory and then transform the subtraction problem into an equivalent convex problem by introducing a series of auxiliary variables.On this basis

an iterative algorithm to obtain the optimal solutions was proposed.Simulation results verify the fast convergence of the proposed algorithm and show that the proposed resource allocation scheme can achieve a higher CEE by comparing with other schemes.

关键词

Keywords

references

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