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NOMA-MEC系统中基于改进遗传算法的协作式计算卸载与资源管理

周天清 胡海琴 曾新亮

周天清, 胡海琴, 曾新亮. NOMA-MEC系统中基于改进遗传算法的协作式计算卸载与资源管理[J]. 电子与信息学报, 2022, 44(9): 3014-3023. doi: 10.11999/JEIT220306
引用本文: 周天清, 胡海琴, 曾新亮. NOMA-MEC系统中基于改进遗传算法的协作式计算卸载与资源管理[J]. 电子与信息学报, 2022, 44(9): 3014-3023. doi: 10.11999/JEIT220306
ZHOU Tianqing, HU Haiqin, ZENG Xinliang. Cooperative Computation Offloading and Resource Management Based on Improved Genetic Algorithm in NOMA-MEC Systems[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3014-3023. doi: 10.11999/JEIT220306
Citation: ZHOU Tianqing, HU Haiqin, ZENG Xinliang. Cooperative Computation Offloading and Resource Management Based on Improved Genetic Algorithm in NOMA-MEC Systems[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3014-3023. doi: 10.11999/JEIT220306

NOMA-MEC系统中基于改进遗传算法的协作式计算卸载与资源管理

doi: 10.11999/JEIT220306
基金项目: 国家自然科学基金(61861017, 61861018, 61961020, 62171119),国家重点研究开发计划(2020YFB1807201)
详细信息
    作者简介:

    周天清:男,副教授,研究方向为超密集组网、移动边缘计算

    胡海琴:女,硕士生,研究方向为超密集组网、移动边缘计算

    曾新亮:男,硕士生,研究方向为超密集组网、移动边缘计算

    通讯作者:

    周天清 zhoutian930@163.com

  • 中图分类号: TN929.5

Cooperative Computation Offloading and Resource Management Based on Improved Genetic Algorithm in NOMA-MEC Systems

Funds: The National Natural Science Foundation of China (61861017, 61861018, 61961020, 62171119), The National Key Research and Development Program of China (2020YFB1807201)
  • 摘要: 为平衡网络负载与充分利用网络资源,针对超密集异构的多用户和多任务边缘计算网络,在用户时延约束下,该文构造了协作式计算任务卸载与无线资源管理的联合优化问题以最小化系统能耗。问题建模时,为应对基站超密集部署导致的严重干扰问题,该文采用了频带划分机制,并引入了非正交多址技术(NOMA)以提升上行频谱利用率。鉴于该目标优化问题具备非线性混合整数的形式,根据多样性引导变异的自适应遗传算法(AGADGM),设计出了协作式计算卸载与资源分配算法。仿真结果表明,在严格满足时延约束条件下,该算法能获取较其他算法更低的系统能耗。
  • 图  1  系统模型

    图  2  用户最大发射功率$p_k^{\max }$对系统总能耗和系统支持率的影响

    图  3  用户密度$\rho $对系统总能耗和系统支持率的影响

    图  4  频带划分因子$\mu $对系统总能耗和系统支持率的影响

    图  5  最优个体的适应度值在遗传算法下的搜索情况

    表  1  参数设置

    参数数值参数数值
    系统带宽$W$20 MHz用户计算能力$F_k^{{\text{UE}}}$1 GHz
    子信道带宽$w$15 kHz截止时延$T_k^{{\text{max}}}$5~10 s
    噪声功率谱密度${\sigma ^2}$–174 dBm/Hz用户最大发射功率$p_k^{\max }$23 dBm
    用户$k$的任务数${M_k}$3~7 个基站计算能力${F^{{\text{MBS}}}}$, ${F^{{\text{SBS}}}}$20 GHz
    单个任务数据大小${d_k}$200~500 kB种群大小$I$64
    单个任务执行时所需的CPU周期数${c_k}$50~100 cycles/bit
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-22
  • 录用日期:  2022-08-09
  • 修回日期:  2022-08-08
  • 网络出版日期:  2022-08-12
  • 刊出日期:  2022-09-19

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