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基于NOMA的移动边缘计算系统公平能效调度算法

胡晗 鲍楠 凌章 沈乐

胡晗, 鲍楠, 凌章, 沈乐. 基于NOMA的移动边缘计算系统公平能效调度算法[J]. 电子与信息学报, 2021, 43(12): 3563-3570. doi: 10.11999/JEIT200898
引用本文: 胡晗, 鲍楠, 凌章, 沈乐. 基于NOMA的移动边缘计算系统公平能效调度算法[J]. 电子与信息学报, 2021, 43(12): 3563-3570. doi: 10.11999/JEIT200898
Han HU, Nan BAO, Zhang LING, Le SHEN. Fair Energy Efficiency Scheduling in NOMA-Based Mobile Edge Computing[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3563-3570. doi: 10.11999/JEIT200898
Citation: Han HU, Nan BAO, Zhang LING, Le SHEN. Fair Energy Efficiency Scheduling in NOMA-Based Mobile Edge Computing[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3563-3570. doi: 10.11999/JEIT200898

基于NOMA的移动边缘计算系统公平能效调度算法

doi: 10.11999/JEIT200898
基金项目: 国家自然科学基金(61871446, 61801244),江苏省科技厅自然科学基金项目(BK20191378),江苏省高等学校自然科学研究面上项目(18KJB510034)
详细信息
    作者简介:

    胡晗:女,1985年生,副教授,研究方向为无线通信网络资源管理及优化等

    鲍楠:女,1985年生,讲师,研究方向为异构网络资源优化及干扰抑制等

    凌章:男,1993年生,硕士,研究方向为边缘计算及动态资源分配等

    沈乐:男,1997年生,硕士生,研究方向为边缘计算及动态资源分配等

    通讯作者:

    胡晗 han_h@njupt.edu.cn

  • 中图分类号: TN929.5

Fair Energy Efficiency Scheduling in NOMA-Based Mobile Edge Computing

Funds: The National Natural Science Foundation of China (61871446, 61801244), The National Science Foundation Program of Jiangsu Province (BK20191378), The National Science Research Project of Jiangsu Higher Education Institutions (18KJB510034)
  • 摘要: 将移动边缘计算技术(MEC)与非正交多址技术(NOMA)结合,同时考虑公平性,该文研究了采用NOMA上行部分卸载的MEC系统公平能效问题。首先将基于公平函数的用户速率与功耗比值定义为公平能效函数,随后提出了两种公平能效调度准则下的能效调度算法,即最大化最小速率准则下DK-SCA算法及最大化系统能效准则下DK-SCALE算法,通过算法实现分别得到两种公平能效调度准则下用户最佳本地CPU处理频率及最佳传输功率。最后通过仿真表明,与基准方案相比,所提基于NOMA的部分卸载方案能够有效地将本地计算和基于NOMA的边缘卸载结合,达到最佳的公平能效性能。
  • 图  1  系统模型

    图  2  最大化最小速率准则下3种方案系统能效对比分析

    图  3  最大化最小速率准则下3种方案下系统速率对比分析

    图  4  最大化最小速率准则下3种方案的系统功耗对比分析

    图  5  最大化系统能效准则下3种方案系统能效对比分析

    图  6  最大化系统能效准则下3种方案的系统速率对比分析

    图  7  最大化系统能效准则下3种方案的系统功耗对比分析

    表  1  DK-SCA迭代算法

     步骤1:初始化本地计算速度$ f_n^{(0)} $和$ x_n^{{\text{u}}(0)} $,$ {Z^0} $,$ \eta _\infty ^0{\text{ = }}0 $,设置
         停止阈值e,迭代次数I
     步骤2: for i=1: I
         利用SCA迭代求解${{\text{P}}_{1.4}}$,得到结果
         $ \left\{ {f_n^i,x_n^{{\text{u}},i},\eta _\infty ^i{\text{,z}}_n^{u,i}{\text{(k)}}} \right\} $,更新能效暂态值
         $\eta _\infty ^i{\text{ = } }\dfrac{ { {Z^i} } }{ {\displaystyle\sum\nolimits_{n \in N} {\left( {\zeta {\text{exp} }\left( { {{x} }_n^{u,i} } \right) + {P_r} + \varepsilon f_n^{i3} } \right)} } }\qquad\qquad (19)$
     步骤3:if$||\eta _\infty ^i - \eta _\infty ^{ {\text{i - 1} } }|| \le e$
         获得最佳能效$ \eta _\infty ^*{\text{ = }}\eta _\infty ^i\; $;
         break;
     步骤4:输出最佳能效$ \eta _\infty ^{\text{*}} $。
    下载: 导出CSV

    表  2  DK-SCALE迭代算法

     步骤1:取$\zeta > 0,{R}_{n}^{\mathrm{min} } > 0,{P}_{n}^{\text{th} }> 0,{\eta }_{0}^{i}$;初始化${{\text{P}}_{2.3}}$,
         $ Z(0) $,$ {f_n}(0) $,$ p_n^{\text{u}}(0) $,$ {a_n}(0) $,$ {b_n}(0) $迭代次数I
     步骤2:for i =1: I
         利用SCALE方法交替迭代求解${{\text{P}}_{2.3}}$,得到近似能效:
         $\eta _0^i = \frac{{\displaystyle\sum\nolimits_{n \in N} {\left( {W{Z_n}(x_n^u,{a_n},{b_n}) + \frac{{{f_n}}}{{{\gamma _n}}}} \right)} }}{{\displaystyle\sum\nolimits_{n \in N} {\left( {\zeta \exp \left( {x_n^u} \right) + {p_r} + \varepsilon f_n^3} \right)} }} \qquad (29)$
     步骤3:if$||\eta _0^i - \eta _0^{i - 1}|| \le e$
         获得最佳能效$ \eta _0^*{\text{ = }}\eta _0^i\; $;
         break;
     步骤4:输出$( {}{f}_{n}^{*},{p}_{n}^{\text{u}*}\text{} )$和最佳能效$ \eta _0^{\text{*}} $。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-10-20
  • 修回日期:  2021-04-29
  • 网络出版日期:  2021-11-10
  • 刊出日期:  2021-12-21

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