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基于干扰消除辅助稀疏连接神经网络的大规模MIMO信号检测

申滨 阳建 曾相誌 崔太平

申滨, 阳建, 曾相誌, 崔太平. 基于干扰消除辅助稀疏连接神经网络的大规模MIMO信号检测[J]. 电子与信息学报, 2023, 45(1): 208-217. doi: 10.11999/JEIT211276
引用本文: 申滨, 阳建, 曾相誌, 崔太平. 基于干扰消除辅助稀疏连接神经网络的大规模MIMO信号检测[J]. 电子与信息学报, 2023, 45(1): 208-217. doi: 10.11999/JEIT211276
SHEN Bin, YANG Jian, ZENG Xiangzhi, CUI Taiping. Massive MIMO Signal Detection Based on Interference Cancellation Assisted Sparsely Connected Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(1): 208-217. doi: 10.11999/JEIT211276
Citation: SHEN Bin, YANG Jian, ZENG Xiangzhi, CUI Taiping. Massive MIMO Signal Detection Based on Interference Cancellation Assisted Sparsely Connected Neural Network[J]. Journal of Electronics & Information Technology, 2023, 45(1): 208-217. doi: 10.11999/JEIT211276

基于干扰消除辅助稀疏连接神经网络的大规模MIMO信号检测

doi: 10.11999/JEIT211276
基金项目: 国家自然科学基金(62071078)
详细信息
    作者简介:

    申滨:男,教授,研究方向为认知无线电、大规模MIMO信号检测等

    阳建:男,硕士生,研究方向为深度学习、大规模MIMO信号检测

    曾相誌:男,硕士生,研究方向为深度学习、大规模MIMO信号检测

    崔太平:男,讲师,研究方向为认知无线电、车联网

    通讯作者:

    申滨 shenbin@cqupt.edu.cn

  • 中图分类号: TN929.5

Massive MIMO Signal Detection Based on Interference Cancellation Assisted Sparsely Connected Neural Network

Funds: The National Natural Science Foundation of China (62071078)
  • 摘要: 近年来,深度学习成为无线通信领域的关键技术之一。在基于深度学习的一系列MIMO信号检测算法中,大多未充分考虑相邻天线之间的干扰消除问题,无法彻底消除多用户干扰对误码率性能的影响。为此,该文提出一种将深度学习与串行干扰消除(SIC)算法进行结合的方法用于大规模MIMO系统上行链路信号检测。首先,通过优化传统的检测网络(DetNet)及改进ScNet检测算法,该文提出一种基于深度神经网络(DNN)的检测算法,称为ImpScNet。在此基础上,进一步将SIC思想应用到深度学习框架结构设计中,提出一种基于深度学习的大规模MIMO多用户SIC检测算法,称为ImpScNet-SIC。此算法在每个检测层上分为两级,其中,第1级由该文提出的ImpScNet算法提供初始解,再将初始解解调至相应的星座点上作为SIC的输入,由此构成该算法的第2级。此外,在SIC中也使用了ImpScNet算法估计传输符号,以便获得最优性能。仿真结果表明,与已有的各种典型代表算法相比,该文所提ImpScNet-SIC检测算法特别适合大规模MIMO信号检测,具有收敛速度快、收敛稳定及复杂度相对较低的优势,并且在10–3误码率上有至少0.5 dB以上的增益。
  • 图  1  DetNet第$l$层网络结构

    图  2  ScNet的第$l$层网络结构

    图  3  ImpScNet-SIC算法处理框架

    图  4  算法复杂度对比

    图  5  32×32($\eta = 1$)天线配置的链路误码率

    图  6  64×32($\eta = 0.5$)天线配置的链路误码率

    图  7  64×64($\eta = 1$)天线配置的链路误码率

    图  8  128×64($\eta = 0.5$)天线配置的链路误码率

    图  9  128×100($\eta = 0.78$)天线配置链路误码率

    图  10  128×128($\eta = 1$)天线配置的链路误码率

    图  11  SNR = 8 dB时,网络的收敛速度

    表  1  MIMO信号检测算法对比

    算法分类算法名称对比总结
    传统检测算法线性检测算法MF[20], ZF[7], MMSE[8](1) ML性能最优,但复杂度呈指数级上升;(2) SD性能次优,是以牺牲复杂度为代价;(3) 其他算法复杂度较低,但性能有待提高
    非线性检测算法ML[3],干扰消除算法[5, 6],SD[4]
    基于深度学习
    检测算法
    学习类算法DetNet[17], ScNet[19], LISA[13](1) DetNet对天线数量有严格要求,复杂度偏高;(2) ScNet受网络稀疏性影响,仅在大规模才表现出较好的性能;(3) LISA仅适用于常规的MIMO信号检测
    消息传递类算法OAMPNet[14], DNN-dBP[16], DNN-MS[16](1) OAMPNet可调参数少,容易训练,但需假设信道矩阵是酉不变矩阵;(2) DNN-dBP和DNN-MS涉及可调参数较多,复杂度偏高
    可训练类算法TPG[21], TAMP[22](1) TPG主要针对下行链路过载信道;(2) TAMP采用全连接作预处理,可调参数多,复杂度偏高
    下载: 导出CSV
    算法1 ImpScNet-SIC检测算法训练流程
     输入:${\boldsymbol{x}}$, ${\boldsymbol{y}}$, ${\boldsymbol{H}}$, $L$, $\alpha $, $\beta $, $t$
     输出:${{\stackrel\smile{x} } }_l$
     (1) 初始化:${ { {\hat {\boldsymbol x} } }_0}{\text{ = } }{{ {\textit{0}}} }$,$L = 15$,$\alpha = 0.2$,$\beta = 0.5$,$t = 0.1$
     (2) 输入各项参数,训练:$ {\boldsymbol{\theta }} = \left\{ {{{\boldsymbol{w}}_l},{{\boldsymbol{b}}_l}} \right\}_{l = 1}^L $ ,使得损失函数最
       小,得到初步估计值
        ${ {\boldsymbol{c} }_l} = {\left[ { { {\boldsymbol{H} }^{\text{T} } }{\boldsymbol{Hy} },{\text{diag} }({ {\boldsymbol{H} }^{\text{T} } }{\boldsymbol{H} }){ {\hat {\boldsymbol{x} } }_{l - 1} },{ {\hat {\boldsymbol{x} } }_{l - 1} } } \right]^{\text{T} } } $
        ${\psi _t}(x) = - 1 + \dfrac{ {\rho (x + t)} }{ {|t|} } - \dfrac{ {\rho (x - t)} }{ {|t|} }$
         $ { { {{\hat {\boldsymbol x}} } }_l} = {\psi _t}\left( { { {\boldsymbol{w} }_l}{ {\boldsymbol{c} }_l} + { {\boldsymbol{b} }_l} } \right) $
         $ {\hat {\boldsymbol{x} } _l} = \alpha {\hat {\boldsymbol{x} } _{l - 1} } + (1 - \alpha ){\hat {\boldsymbol{x} } _l} $
        $\mathcal{L}({\boldsymbol{x} },\hat {\boldsymbol{x} } ) = \displaystyle\sum\limits_{l = 1}^L {\ln } (l)\left[ { { {\left\| { {\boldsymbol{x} } - { { {\boldsymbol{\hat x} } }_l} } \right\|}^2} + \beta r({ {\hat {\boldsymbol{x} } }_l},{\boldsymbol{x} })} \right]$
     (3) 将得到的初步估计值解调到相应的星座点上
        ${\tilde x_j} = \mathop {\arg \min }\limits_{i \in \{ 1,2, \cdots ,{2^K}\} } |{\hat x_l}(j) - {s_i}|,j = 1,2, \cdots ,2M$
     (4) 再引入SIC,根据信道矩阵${\boldsymbol{H}}$列范数的大小来进行降序排序
        $\mathcal{C} = \arg {{\rm{sort}}} \left( { {\gamma _1},{\gamma _2}, \cdots ,{\gamma _M} } \right)$
         $ {\gamma _m} = \left\| {{{\boldsymbol{h}}_m}} \right\|_2^2,\forall \:m = 1,2, \cdots ,M $
     (5) 消除第$i (i = 1,2, \cdots ,2M)$个用户对下一个接收信号的影响,
       并更新接收信号
        ${ { { {\tilde {\boldsymbol y} } } }_i} = \displaystyle\sum\limits_{k = 1}^i { { {\boldsymbol{h} }_k} } ({x_k} - { {\tilde x}_k}) + \displaystyle\sum\limits_{j = i + 1}^{2M} { { {\boldsymbol{h} }_j} } {x_j}$
        $ {{{{\tilde {\boldsymbol y}}}}_{i + 1}} = {{{\boldsymbol{\tilde y}}}_i} - {{\boldsymbol{h}}_{i + 1}}{{\tilde x}_{i + 1}}\left( {i = 1,2, \cdots ,2M} \right) $
     (6) 得到更新的接收向量$ {{\tilde {\boldsymbol y}}} $、传输向量$ {{\tilde {\boldsymbol x}}} $和信道矩阵${\boldsymbol{H}}$再执行步
       骤(2)
     (7) 重复步骤(3)—步骤(6),直到所有有用信号均被检测出来,
       得到最终的检测信号向量${ { {\stackrel\smile{\boldsymbol{x} } } }_l}$
    下载: 导出CSV

    表  2  训练ImpScNet-SIC时参数设置

    参数名称具体设置
    发射天线数$M$4, 32, 64, 100, 128
    接收天线数$N$4, 32, 64, 128
    层数$L$15,15
    起始学习率${\beta _0}$0.001
    学习衰减率${\beta _t}$0.97
    SNR(dB)范围(4,14)
    批量大小500
    训练网络迭代次数10000
    迭代周期5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-16
  • 修回日期:  2022-03-28
  • 网络出版日期:  2022-04-18
  • 刊出日期:  2023-01-17

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