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基于模型驱动深度学习的OTFS信道估计

蒲旭敏 刘雁翔 宋米雪 陈前斌

蒲旭敏, 刘雁翔, 宋米雪, 陈前斌. 基于模型驱动深度学习的OTFS信道估计[J]. 电子与信息学报, 2024, 46(2): 680-687. doi: 10.11999/JEIT230072
引用本文: 蒲旭敏, 刘雁翔, 宋米雪, 陈前斌. 基于模型驱动深度学习的OTFS信道估计[J]. 电子与信息学报, 2024, 46(2): 680-687. doi: 10.11999/JEIT230072
PU Xumin, LIU Yanxiang, SONG Mixue, CHEN Qianbin. Orthogonal Time Frequency Space Channel Estimation Based on Model-driven Deep Learning[J]. Journal of Electronics & Information Technology, 2024, 46(2): 680-687. doi: 10.11999/JEIT230072
Citation: PU Xumin, LIU Yanxiang, SONG Mixue, CHEN Qianbin. Orthogonal Time Frequency Space Channel Estimation Based on Model-driven Deep Learning[J]. Journal of Electronics & Information Technology, 2024, 46(2): 680-687. doi: 10.11999/JEIT230072

基于模型驱动深度学习的OTFS信道估计

doi: 10.11999/JEIT230072
基金项目: 国家自然科学基金(61701062),中国博士后科学基金(2019M651649),江苏省博士后科研基金(2018K041c),重庆市教育委员会科学技术研究项目(KJQN202100649, KJQN202000612)
详细信息
    作者简介:

    蒲旭敏:男,副教授,硕士生导师,研究方向为通信、信号处理和信息理论,当前聚焦超大规模MIMO和OTFS

    刘雁翔:男,硕士生,研究方向为正交时频空间调制、无线通信信道估计

    宋米雪:女,硕士生,研究方向为正交时频空间调制、无线通信信号检测

    陈前斌:男,教授,博士生导师,研究方向为个人通信、多媒体信息处理与传输、下一代移动通信网络

    通讯作者:

    蒲旭敏 puxm@cqupt.edu.cn

  • 中图分类号: TN92

Orthogonal Time Frequency Space Channel Estimation Based on Model-driven Deep Learning

Funds: The National Natural Science Foundation of China (61701062), The China Postdoctoral Science Foundation (2019M651649), The Jiangsu Planned Projects for Postdoctoral Research Funds (2018K041c), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202100649, KJQN202000612)
  • 摘要: 针对单输入单输出(SISO)的正交时频空间(OTFS)调制系统,该文利用一种模型驱动深度学习算法进行OTFS信道估计。该方案首先将去噪近似消息传递(DAMP)算法进行深度展开,利用去噪卷积神经网络代替传统的去噪器,对含噪的时延多普勒信道进行去噪估计,然后提供了状态演化方程来预测可学习去噪近似消息传递(LDAMP)算法的理论归一化均方误差性能。仿真结果表明,相比于其他估计方案,该方案不仅在低信噪比条件下具有优越的性能表现,而且还具有非常好的鲁棒性,在信道路径总数不变时,增加OTFS 2维网格点数量,可以有效提升信道估计精确度。
  • 图  1  OTFS调制系统模型框图[17]

    图  2  LDAMP第$l$层网络结构示意图

    图  3  DnCNN网络架构示意图

    图  4  LDAMP算法在不同速度下的NMSE随SNR变化的对比

    图  5  不同算法NMSE随SNR变化的对比

    图  6  LDAMP算法仿真NMSE与理论NMSE随SNR变化的对比

    图  7  LDAMP算法仿真NMSE与理论NMSE随迭代次数变化的对比

    算法1 基于OTFS系统的LDAMP算法
     输入:测量矩阵:$ {\mathbf{X}} $;观测矢量:$ {\mathbf{y}} $;迭代次数:$ L $
     初始化:$ {{\mathbf{z}}^0} = {\mathbf{y}} $,$ {\hat \sigma ^0} = ||{{\mathbf{z}}^0}|{|_2}/\sqrt {MN} $, $ {\mathbf{\hat h}}_{{\text{eff}}}^0 = 0 $
     for $ l = 0,1, \cdots ,L - 1 $ do
      (1) $ {\mathbf{\hat h}}_{{\text{eff}}}^{l + 1} = {D_{{{\hat \sigma }^l}}}({\mathbf{\hat h}}_{{\text{eff}}}^l + {{\mathbf{X}}^{\text{T}}}{{\mathbf{z}}^l}) $
      (2) $ {{\mathbf{c}}^{l + 1}} = {{\mathbf{z}}^l}{\text{div}}{D_{{{\hat \sigma }^l}}}({\mathbf{\hat h}}_{{\text{eff}}}^l + {{\mathbf{X}}^{\text{T}}}{{\mathbf{z}}^l})/MN $
      (3) $ {{\mathbf{z}}^{l{\text{ + }}1}} = {\mathbf{y}} - {\mathbf{X\hat h}}_{{\text{eff}}}^{l{\text{ + }}1} + {{\mathbf{c}}^{l + 1}} $
      (4) $ {\hat \sigma ^{l{\text{ + }}1}} = ||{{\mathbf{z}}^{l{\text{ + }}1}}|{|_2}/\sqrt {MN} $
     end for
     输出:时延多普勒信道估计值$ {{\mathbf{\hat h}}_{{\text{eff}}}} = {\mathbf{\hat h}}_{{\text{eff}}}^L $
    下载: 导出CSV

    表  1  不同算法复杂度对比结果

    所用算法复杂度
    LDAMPO(L(MN+M2N2))
    DAMPO(L(MN+M2N2))
    OMPO(L(M6N6))
    下载: 导出CSV

    表  2  主要仿真参数设置

    参数名称参数设置
    载波数$ M $$ 4,8,12 $
    符号数$ N $$ 4,8,12 $
    子载波间隔$ \Delta f $$15{\text{ kHz} }$
    载波频率$ {f_{\text{c}}} $$ 4{\text{ GHz}} $
    带宽B0.18 MHz
    波长75 mm
    调制方式4-QAM
    信道模型EVA
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-02-20
  • 修回日期:  2023-05-15
  • 网络出版日期:  2023-05-23
  • 刊出日期:  2024-02-10

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