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基于分布式联邦学习的毫米波通信系统波束配置方法

薛青 来东 徐勇军 闫莉

薛青, 来东, 徐勇军, 闫莉. 基于分布式联邦学习的毫米波通信系统波束配置方法[J]. 电子与信息学报, 2024, 46(1): 138-145. doi: 10.11999/JEIT221536
引用本文: 薛青, 来东, 徐勇军, 闫莉. 基于分布式联邦学习的毫米波通信系统波束配置方法[J]. 电子与信息学报, 2024, 46(1): 138-145. doi: 10.11999/JEIT221536
XUE Qing, LAI Dong, XU Yongjun, YAN Li. Beam Configuration for Millimeter Wave Communication Systems Based on Distributed Federated Learning[J]. Journal of Electronics & Information Technology, 2024, 46(1): 138-145. doi: 10.11999/JEIT221536
Citation: XUE Qing, LAI Dong, XU Yongjun, YAN Li. Beam Configuration for Millimeter Wave Communication Systems Based on Distributed Federated Learning[J]. Journal of Electronics & Information Technology, 2024, 46(1): 138-145. doi: 10.11999/JEIT221536

基于分布式联邦学习的毫米波通信系统波束配置方法

doi: 10.11999/JEIT221536
基金项目: 国家自然科学基金(62001071, 62101460, 62271094),澳门青年学者计划(AM2021018),重庆市教委科学技术研究项目(KJZD-K202200601),中国博士后科学基金(2022MD723725)
详细信息
    作者简介:

    薛青:女,博士,硕士生导师,讲师,研究方向为毫米波无线通信、智能无线网络、移动网络中的资源管理等

    来东:男,硕士生,研究方向为毫米波无线通信等

    徐勇军:男,博士,副教授,博士生导师,研究方向为D2D通信、鲁棒性资源分配等

    闫莉:女,博士,副教授,研究方向为轨道交通移动通信、毫米波通信等

    通讯作者:

    薛青 xueq@cqupt.edu.cn

  • 中图分类号: TN92

Beam Configuration for Millimeter Wave Communication Systems Based on Distributed Federated Learning

Funds: The National Natural Science Foundation of China (62001071, 62101460, 62271094), Macao Young Scholars Program (AM2021018), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601), China Postdoctoral Science Foundation (2022MD723725)
  • 摘要: 针对超密集组网中毫米波通信系统复杂的波束配置问题,该文提出一种基于分布式联邦学习(DFL)的波束配置算法(BMDFL),旨在利用有限的波束资源实现用户覆盖率最大化。考虑到传统集中式学习存在用户数据安全问题,基于分布式联邦学习框架构建系统模型,从而减少用户隐私信息的泄露。为了实现波束的智能化配置,引入双深度Q学习算法(DDQN)训练系统模型,并通过马尔可夫决策过程将长期的动态优化问题转化为相应的数学模型进行求解。仿真结果从系统的网络吞吐量和用户覆盖率方面验证了该方法的有效性和鲁棒性。
  • 图  1  系统模型

    图  2  基于DDQN的BMDFL框架

    图  3  BMDFL算法收敛性

    图  4  BMDFL性能随用户密度和SINR阈值的变化

    图  5  BMDFL在不同基站密度下的网络吞吐量性能

    图  6  BMDFL,FDBM和CBM网络吞吐量性能对比

    图  7  BMDFL,FDBM和CBM用户覆盖率性能对比

    算法1 基于分布式联邦的毫米波通信系统波束配置算法
     输入:$ \mathcal{U} $,$ \mathcal{M} $, ${\varPhi _m}$,$ \kappa $, $ {S_t} $, $ {A_t} $,$ \gamma $, $ \rho $,$ \lambda $, $ \tau $
     输出:波束配置策略$ \mathcal{C}{\text{(}}t{\text{)}} $
     (1) 初始化经验回访池,初始化全局模型
     (2) for j = 1 : 1 : J do
     (3)  $\triangleright $模型初始化
     (4)  根据式(4)初始化局部模型
     (5)  $\triangleright $数据筛选
     (6)  通过${d_{u,m} } \le {\varPhi _m}$和 $ {{{k_{u,m}}} \mathord{\left/ {\vphantom {{{k_{u,m}}} {{k_{\mathcal{U},m}}}}} \right. } {{k_{\mathcal{U},m}}}} $筛选有效的用户样本参与
        模型训练
     (7)  $\triangleright $局部模型训练
     (8)  for t = 1 : 1 : $ \tau $ do
     (9)   通过贪心算法求解$ {A_t} = \arg {\max _{{A_t}}}Q({S_t},{A_t}) $
     (10)   执行$ {A_t} $得到$ {\mathcal{R}_t} $,$ {S_{t + 1}} $
     (11)   将$ ({S_t},{A_t},{\mathcal{R}_t},{S_{t + 1}}) $存储到经验回访池
     (12)   从经验池中随机抽取样本进行训练
     (13)   根据式(5)计算$Y_t^{{\rm{DDQN}}}$
     (14)   根据式(7)更新$ {\vartheta _{t,m}} $
     (15)   end for
     (16)   $\triangleright $临时中心控制节点选择
     (17)   通过${T_m}{\text{ = } }\min\left( { {T_1},{T_2}, \cdots ,{T_M} } \right)$选择执行时间最短的
         mBS m
     (18)   $\triangleright $全局模型更新
     (19)   根据式(9)更新全局模型
     (20) end for
     (21)得到全局最优的波束配置策略$ \mathcal{C}(t) $
    下载: 导出CSV

    表  1  神经网络结构设置

    层数结构
    1nn.Sequential(nn.Linear($ \left| \mathcal{U} \right| $+2*$ {b_m} $, 40), nn.ReLU())
    2nn.Sequential(nn.Linear(40,60), nn.ReLU())
    3nn.Sequential(nn.Linear(60,40), nn.ReLU())
    4nn.Sequential(nn.Linear(40,1))
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-13
  • 修回日期:  2023-05-08
  • 网络出版日期:  2023-05-17
  • 刊出日期:  2024-01-17

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