Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network

Liqin SHI; Yinghui YE; Guangyue LU

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

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Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network

Topics: Convergence of Communications and Computing for the IoE | 更新时间：2024-06-05

- Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network
- Journal on Communications Vol. 41, Issue 10, Pages: 59-69(2020)
- 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：
- 基金信息：
  
  The Science and Technology Innovation Team of Shaanxi Province(2017KCT-30-02);The National Natural Science Foundation of China(61901367);The Natural Science Foundation of Shaanxi Province(2020JQ-844)
- DOI：10.11959/j.issn.1000-436x.2020182
  CLC： TN92
- Online First：2020-10，
  
  Published：25 October 2020
- 稿件说明：
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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：

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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