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基于深度学习的下行大规模MIMO OFDM系统的1比特预编码算法

周宸颢 温利嫄 钱骅 康凯

周宸颢, 温利嫄, 钱骅, 康凯. 基于深度学习的下行大规模MIMO OFDM系统的1比特预编码算法[J]. 电子与信息学报, 2024, 46(3): 886-894. doi: 10.11999/JEIT230239
引用本文: 周宸颢, 温利嫄, 钱骅, 康凯. 基于深度学习的下行大规模MIMO OFDM系统的1比特预编码算法[J]. 电子与信息学报, 2024, 46(3): 886-894. doi: 10.11999/JEIT230239
ZHOU Chenhao, WEN Liyuan, QIAN Hua, KANG Kai. 1-bit Precoding Algorithm for Massive MIMO OFDM Downlink Systems with Deep Learning[J]. Journal of Electronics & Information Technology, 2024, 46(3): 886-894. doi: 10.11999/JEIT230239
Citation: ZHOU Chenhao, WEN Liyuan, QIAN Hua, KANG Kai. 1-bit Precoding Algorithm for Massive MIMO OFDM Downlink Systems with Deep Learning[J]. Journal of Electronics & Information Technology, 2024, 46(3): 886-894. doi: 10.11999/JEIT230239

基于深度学习的下行大规模MIMO OFDM系统的1比特预编码算法

doi: 10.11999/JEIT230239
基金项目: 国家重点研究发展计划(2020YFB2205603) ,国家自然科学基金(61971286)
详细信息
    作者简介:

    周宸颢:男,博士生,研究方向为无线通信

    温利嫄:女,博士生,研究方向为无线通信

    钱骅:男,研究员,研究方向为无线通信、非线性信号处理、大数据信号处理

    康凯:男,正高级工程师,研究方向为无线通信、通信系统设计、物联网技术

    通讯作者:

    康凯 kangk@sari.ac.cn

  • 中图分类号: TN929.5; TP181

1-bit Precoding Algorithm for Massive MIMO OFDM Downlink Systems with Deep Learning

Funds: The National Key Research and Development Program of China (2020YFB2205603),The National Natural Science Foundation of China (61971286)
  • 摘要: 大规模多输入多输出(MIMO)系统中通过在基站端配备数百根天线,在提高频谱利用效率的同时,也带来了系统成本的增加。本课题组之前提出了一种适用于下行大规模MIMO正交频分复用(OFDM)系统的收敛保证的多载波1比特预编码算法(CG-MC1bit),能够获得较优的系统性能,但相应的计算复杂度较高,阻碍了其在实时系统中的应用。为进一步解决大规模MIMO系统中的成本和功耗问题,该文提出了一个模型驱动的神经网络,在CG-MC1bit算法的基础上迭代展开(Unfolding)得到了一种更加高效的CG-MC1bit-Net算法。具体而言,将迭代算法展开为一个神经网络,并引入可训练的参数来替代前向传播中的高复杂性操作。实验结果表明,该方法能够自动更新参数,与传统的预编码算法相比,收敛速度更快,计算复杂度更低。
  • 图  1  系统模型

    图  2  数据流图

    图  3  CG-MC1bit与CG-MC1bit-Net算法收敛性对比

    图  4  不同学习率下的收敛速度

    图  5  损失函数的下降趋势

    图  6  当$ M = {\text{8 , }}{N_{\text{a}}} = {\text{128}} $时各算法的误码率性能对比

    图  7  当$ M = {\text{8 , }}{N_{\text{a}}} = {\text{512}} $时各算法的误码率性能对比

    图  8  当$ {\text{ }}{N_{\text{a}}} = {\text{128}} $时不同用户数的误码率性能对比

    1  CG-MC1bit-Net算法

     已知:传输信号矩阵$ {{\boldsymbol{S}}} $,信道矩阵$ {{\boldsymbol{H}}} $,神经网络层数$ K $
     1) 初始化 $ {\boldsymbol{X}}^{\text{0}}={{\bf{1}}},\;{\boldsymbol{R}}^{\text{0}}\text{=}{{\bf{1}}},\;{\boldsymbol{V}}^{\text{0}}={{ {\textit{0}}}},\;\alpha =\text{0}\text{.1} $
     2) for 数据集中的每个样本 do
     3)  初始化$k = {\text{0}}$
     4)  while $k < K$ do
     5)   ${\tilde {\boldsymbol{H}}} = {\alpha ^k}{{\boldsymbol{H}}}$
     6)   初始化$l = {\text{0}}$
     7)   for $l < L$ do
     8)    根据式(4)更新$ {{{\boldsymbol{x}}}^{k + 1}}[l] $,$ l = l + {\text{1}} $
     9)   end for
     10)   $ {{\boldsymbol{R}}}_{\text{T}}^{k + 1} = \eta \tanh \left[ {\delta \dfrac{1}{L}\left( {{{{\boldsymbol{X}}}^{k + 1}} + \dfrac{1}{\lambda }{{{\boldsymbol{V}}}^k}} \right){{\boldsymbol{F}}}_L^{\text{H}}} \right] $
     11)   $ {{{\boldsymbol{R}}}^{k + 1}} = {{\boldsymbol{R}}}_{\text{T}}^{k + 1}{{{\boldsymbol{F}}}_L} $
     12)   $ {{{\boldsymbol{V}}}^{k + 1}} = {{{\boldsymbol{V}}}^k} + \lambda \left( {{{\boldsymbol{X}}}_{\text{T}}^{k + 1} - {{{\boldsymbol{R}}}^{k + 1}}} \right) $
     13)   $ k = k + {\text{1}} $
     14)  end while
     15)  输出$ {{\boldsymbol{R}}}_{\text{T}}^K $,并根据式(11)计算损失函数$ {\text{los}}{{\text{s}}_{{\text{MSE}}}} $
     16)  执行会话
     17)  for 隐藏层或输出层的每个神经元 do
     18)   更新网络中的每一个权值和偏差
     19)  end for
     20) end for
    下载: 导出CSV

    表  1  运行速度对比

    预编码算法 天线数128 天线数512
    CG-MC1bit-Net 0.429 s 2.127 s
    CG-MC1bitx 3.47 s 10.33 s
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-07-18
  • 修回日期:  2023-10-23
  • 网络出版日期:  2023-10-27
  • 刊出日期:  2024-03-27

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