高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

面向数据压缩的NOMA-MEC系统能耗最小化研究

施丽琴 刘璇 卢光跃

施丽琴, 刘璇, 卢光跃. 面向数据压缩的NOMA-MEC系统能耗最小化研究[J]. 电子与信息学报. doi: 10.11999/JEIT231033
引用本文: 施丽琴, 刘璇, 卢光跃. 面向数据压缩的NOMA-MEC系统能耗最小化研究[J]. 电子与信息学报. doi: 10.11999/JEIT231033
SHI Liqin, LIU Xuan, LU Guangyue. Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231033
Citation: SHI Liqin, LIU Xuan, LU Guangyue. Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231033

面向数据压缩的NOMA-MEC系统能耗最小化研究

doi: 10.11999/JEIT231033
基金项目: 国家自然科学基金(62301421)
详细信息
    作者简介:

    施丽琴:女,副教授,研究方向为无线供能通信、移动边缘计算

    刘璇:女,硕士生,研究方向为移动边缘计算

    卢光跃:男,教授,研究方向为宽带无线通信

    通讯作者:

    刘璇 liuxuan202309@126.com

  • 中图分类号: TN926

Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System

Funds: The National Natural Science Foundation of China (62301421)
  • 摘要: 该文研究基于数据压缩的非正交多址-移动边缘计算(NOMA-MEC)系统中系统能耗最小化问题。考虑到部分压缩与卸载方案和基站端计算能力有限等条件,通过联合优化各用户的任务压缩和卸载比例、发射功率以及任务压缩时间等变量,建立一个系统能耗最小化优化问题。为了求解该问题,首先推导出各用户最佳发射功率的闭式表达式。接着利用连续凸逼近(SCA)方法对原问题的非凸约束进行近似,然后提出一个基于SCA的高效迭代算法来求解原问题,从而得到该系统的最佳资源分配方案。最后借助于计算机仿真对所提出方案的性能优势进行验证,仿真结果表明相比于其他基准方案,该文所提方案能有效降低系统能耗。
  • 图  1  系统模型

    图  2  系统时隙图

    图  3  本文所提算法的收敛性

    图  4  不同方案下系统能耗与各用户计算任务量的对比情况

    图  5  不同方案下系统能耗随基站端计算频率的变化情况

    图  6  不同用户接入数量下系统能耗随用户任务量的变化情况

    1  基于SCA的迭代算法

     输入:给定初始值$\left( {\left\{ {\alpha _k^0} \right\},\left\{ {\beta _k^0} \right\},t_{\rm o}^0,t_{\rm c}^0} \right)$;设置迭代次数$n = 1$、最大收敛次数$N$和收敛精度$\varepsilon $;设定循环中止标志${\text{Flag}} = 0$;
     输出:最优解$ \left( {\left\{ {\alpha _k^*} \right\},\left\{ {\beta _k^*} \right\},t_{\rm c}^{^*},\left\{ {p_k^*} \right\},t_{\rm o}^*} \right) $;最小系统能耗${E^*}$。
     (1) 根据$\left( {\left\{ {\alpha _k^0} \right\},\left\{ {\beta _k^0} \right\},t_{\rm o}^0,t_{\rm c}^0} \right)$计算得出中间变量$x_k^0$,进而计算出初始系统能耗为${E^0}$;
     (2) Repeat
     (3)  根据初始值计算得出$z_k^0$;
     (4)  在给定$\left( {\left\{ {z_k^0} \right\},t_{\rm o}^0} \right)$的情况下,利用CVX工具求解问题(21),并得到其最优解,即:$\left( {\left\{ {\alpha _k^n} \right\},\left\{ {x_k^n} \right\},t_{\rm c}^{^n},t_{\rm o}^n} \right)$;
     (5)  根据上述所得到的最优解计算得出此时的最小系统能耗为${E^n}$;
     (6)  if $\left| {{E^n} - {E^{n - 1}}} \right| < \varepsilon $;
     (7)   此时最优解即为问题(18)的最优解,即:$ {\alpha }_{k}^{*}={\alpha }_{k}^{n};{\beta }_{k}^{*}={x}_{k}^{n}/{\alpha }_{k}^{n},\forall k\text{ };{t}_{o}^{*}={t}_{o}^{n};{t}_{c}^{*}={t}_{c}^{n} $;
          $ p_k^ * = {\sigma ^2}/{h_k}\left( {\exp \left( {z_k^0\ln 2/B{t_{\rm o}}} \right) - \exp \left( {z_{k - 1}^0\ln 2/B{t_{\rm o}}} \right)} \right) $;系统最小能耗为${E^*} = {E^n}$;
     (8)   输出问题(17)的最优解和系统最小能耗,即:$\left( {\left\{ {\alpha _k^*} \right\},\left\{ {\beta _k^*} \right\},\left\{ {p_k^*} \right\},t_{\rm o}^*,t_{\rm c}^*} \right)$;${E^*}$;设置${\text{Flag}} = 1$;
     (9)  else
     (10) 设置$ {\alpha }_{k}^{0}={\alpha }_{k}^{n};{x}_{k}^{0}={x}_{k}^{n},\forall k\text{ };{t}_{o}^{0}={t}_{o}^{n} $;$n = n + 1$;
     (11) end
     (12) until ${\text{Flag}} = 1$or$n = N$;
    下载: 导出CSV
  • [1] SONG Zhiyuan, MA Ruijiang, and XIE Yong. A collaborative task offloading strategy for mobile edge computing in internet of vehicles[C]. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2021: 1379–1384. doi: 10.1109/IAEAC50856.2021.9390817.
    [2] SATRIA D, PARK D, and JO M. Recovery for overloaded mobile edge computing[J]. Future Generation Computer Systems, 2017, 70: 138–147. doi: 10.1016/j.future.2016.06.024.
    [3] PAN Yijin, CHEN Ming, YANG Zhaohui, et al. Energy-efficient NOMA-based mobile edge computing offloading[J]. IEEE Communications Letters, 2018, 23(2): 310–313. doi: 10.1109/LCOMM.2018.2882846.
    [4] ZHANG Siqi, YI Na, and MA Yi. Correlation-based device energy-efficient dynamic multi-task offloading for mobile edge computing[C]. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, 2021: 1–5. doi: 10.1109/VTC2021-Spring51267.2021.9448864.
    [5] GAO Mingjin, SHEN Rujing, LI Jun, et al. Computation offloading with instantaneous load billing for mobile edge computing[J]. IEEE Transactions on Services Computing, 2022, 15(3): 1473–1485. doi: 10.1109/TSC.2020.2996764.
    [6] HE Tianmi, WANG Dawei, ZHOU Fuhui, et al. Delay-aware offloading for cooperative NOMA-based near-and-far MEC networks[C]. 2021 IEEE/CIC International Conference on Communications in China (ICCC), Xiamen, China, 2021: 978–983. doi: 10.1109/ICCC52777.2021.9580414.
    [7] SHI Liqin, YE Yinghui, CHU Xiaoli, et al. Computation energy efficiency maximization for a NOMA-based WPT-MEC network[J]. IEEE Internet of Things Journal, 2021, 8(13): 10731–10744. doi: 10.1109/JIOT.2020.3048937.
    [8] REN Jinke, RUAN Yangjun, and YU Guanding. Data transmission in mobile edge networks: Whether and where to compress?[J]. IEEE Communications Letters, 2019, 23(3): 490–493. doi: 10.1109/LCOMM.2019.2894415.
    [9] XU Ding, LI Qun, and ZHU Hongbo. Energy-saving computation offloading by joint data compression and resource allocation for mobile-edge computing[J]. IEEE Communications Letters, 2019, 23(4): 704–707. doi: 10.1109/LCOMM.2019.2897630.
    [10] MHEICH Z and DUPRAZ E. Short length non-binary rate-adaptive LDPC codes for Slepian-Wolf source coding[C]. 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 2018: 1–5. doi: 10.1109/WCNC.2018.8377291.
    [11] LIU Chenglin. Predictor-based synchronization algorithms for multiple harmonic oscillators with communication delay[C]. 2015 IEEE International Conference on Information and Automation, Lijiang, China, 2015: 1003–1008. doi: 10.1109/ICInfA.2015.7279433.
    [12] GRANT M and BOYD S. CVX: Matlab software for disciplined convex programming[EB/OL]. http://cvxr.com/cvx, 2023.
    [13] BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2009: 104–112. (查阅网上资料, 未找到本条文献相关年份信息, 请确认) .

    BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2009: 104–112. (查阅网上资料, 未找到本条文献相关年份信息, 请确认).
    [14] LUO Weiran, SHEN Yanyan, YANG Bo, et al. Joint 3-D trajectory and resource optimization in multi-UAV-enabled IoT networks with wireless power transfer[J]. IEEE Internet of Things Journal, 2021, 8(10): 7833–7848. doi: 10.1109/JIOT.2020.3041303.
  • 加载中
图(6) / 表(1)
计量
  • 文章访问数:  49
  • HTML全文浏览量:  9
  • PDF下载量:  5
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-09-19
  • 修回日期:  2024-03-15
  • 网络出版日期:  2024-04-02

目录

    /

    返回文章
    返回