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一种低复杂度的正交时频空系统接收机设计

廖勇 李雪

廖勇, 李雪. 一种低复杂度的正交时频空系统接收机设计[J]. 电子与信息学报, 2024, 46(6): 2418-2424. doi: 10.11999/JEIT230625
引用本文: 廖勇, 李雪. 一种低复杂度的正交时频空系统接收机设计[J]. 电子与信息学报, 2024, 46(6): 2418-2424. doi: 10.11999/JEIT230625
LIAO Yong, LI Xue. Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2418-2424. doi: 10.11999/JEIT230625
Citation: LIAO Yong, LI Xue. Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2418-2424. doi: 10.11999/JEIT230625

一种低复杂度的正交时频空系统接收机设计

doi: 10.11999/JEIT230625
基金项目: 重庆市自然科学基金(CSTB2023NSCQ-MSX0025)
详细信息
    作者简介:

    廖勇:男,副研究员,研究方向为高速移动通信系统及其关键技术

    李雪:女,硕士生,研究方向为高速移动通信中的信道估计

    通讯作者:

    廖勇 liaoy@cqu.edu.cn

  • 中图分类号: TN929.5

Low Complexity Receiver Design for Orthogonal Time Frequency Space Systems

Funds: Chongqing Natural Science Foundation (CSTB2023NSCQ-MSX0025)
  • 摘要: 正交时频空(OTFS)调制可以将时间和频率选择性信道转换为时延-多普勒(DD)域的非选择性信道,这为高速移动场景建立可靠的无线通信提供了解决方案。然而,在车联网等复杂的多散射场景下,信道存在严重的多普勒间干扰(IDI),这给OTFS接收机信号的准确解调带来了极大的挑战。针对上述问题,该文提出一种联合稀疏贝叶斯学习(SBL)和阻尼最小二乘最小残差(d-LSMR)的OTFS接收机设计。首先,根据OTFS时域和DD域的关系,采用基扩展模型(BEM)将信道估计问题转换为基系数恢复问题,精准估计包括多普勒采样点在内的DD域信道。然后,提出一种高效的转换算法将基系数转换为信道等效矩阵。其次,将信道估计中估计得到的噪声,用于d-LSMR均衡器中进行信道均衡,并利用DD域信道矩阵的稀疏性实现快速收敛。系统仿真结果表明,与目前代表性的OTFS接收机相比,该文所提方案实现了更好的误码率性能,同时降低了计算复杂度。
  • 图  1  OTFS传输系统

    图  2  SBL算法的收敛过程

    图  3  d-LSMR算法的收敛过程

    图  4  调制方式为QPSK时接收机BER性能

    图  5  调制方式为16QAM时接收机BER性能

    算法1 基于SBL学习的基系数估计
     输入:$ {\boldsymbol{y}} $,${\boldsymbol{\varPhi } }$
     输出:$ {\boldsymbol{\tilde c}} $, $ \beta $
     (1) 初始化:${\bar {\boldsymbol{\alpha} } ^{(0)} } = {{\text{0}}}$, $\beta = 1$
     (2) while $||{\bar {\boldsymbol{\alpha} } ^{(i)} } - {\bar {\boldsymbol{\alpha} } ^{(i - 1)} }|| < {\varepsilon _{\boldsymbol{\alpha}} }$
     (3)  for $j = 1,2, \cdots ,{{QL} }$
     (4)   超参数更新:${\bar \alpha ^{\left( i \right)} }\left( j \right) = \varSigma _{jj}^{(i - 1)} + {\left( { {\boldsymbol{\mu} } _{\boldsymbol{c}}^{\left( {i - 1} \right)}\left( j \right)} \right)^2}$
     (5)  end
     (6)  噪声更新:${\beta ^{(i)} } = \frac{ {||{\boldsymbol{y} } - {\boldsymbol{\varPhi \mu } }_c^{(i)}||_2^2} }{ { {N_{\boldsymbol{p}}} - {\boldsymbol{\varSigma } }_{\boldsymbol{c}}^{(i)}(j)(1 - {\alpha ^{(i)} }(j)\varSigma _{jj}^{(i)})} }$
     (7)  后验更新:通过式(7)和式(8),更新后验参数${\boldsymbol{\varSigma}} _{\boldsymbol{c}}^{\left( i \right)}$和${\boldsymbol{\mu}} _{\boldsymbol{c}}^{\left( i \right)}$
     (8)  $i = i + 1$
     (9) end
    下载: 导出CSV
    算法2 基于d-LSMR的基系数均衡
     输入:$ {\boldsymbol{y}} $,$ {\boldsymbol{\tilde c}} $, ${\sigma ^2}$
     输出:$ {\boldsymbol{\tilde x}} $
     (1) for $q = 1,2, \cdots ,Q$
     (2)   ${\boldsymbol{H = H} }{ + }\left( { {\text{circ} }\left( {\left( { {{\boldsymbol{\varXi}} _q} \odot {c_q}\left( 0 \right)} \right), \cdots \left( { {{\boldsymbol{\varXi}} _q} \odot {c_q}\left( {L - 1} \right)} \right)} \right)} \right)$
     (3) end
     (4) 初始化:$ {{\eta }_1}{{\boldsymbol{\nu }}_1} = {\boldsymbol{y}} $, $ {{\xi }_1}{{\boldsymbol{\omega }}_1} = {{\boldsymbol{H}}^{\text{H}}}{{\boldsymbol{\nu }}_1} $, $i = 0$ (d-LSMR均衡)
     (5) while $i < M$ or ${\varepsilon _i}$收敛
     (6)   $ {{\eta }_{i + 1}}{{\boldsymbol{\nu }}_{i + 1}} = {\boldsymbol{H}}{{\boldsymbol{\omega }}_i} - {{\xi }_i}{{\boldsymbol{\nu }}_i} $
     (7)   $ {{\xi }_{i + 1}}{{\boldsymbol{\omega }}_{i + 1}} = {{\boldsymbol{H}}^{\text{H}}}{{\boldsymbol{\nu }}_{i + 1}} - {{\eta }_{i + 1}}{{\boldsymbol{\omega }}_i} $
     (8)   根据式(22)QR分解得到$ {{\boldsymbol{\bar R}}_{m + 1}} $
     (9)   求解(21)得到${{\boldsymbol{z}}_i}$,并计算残量
     (10)   $i = i + 1$
     (11) end
     (12) $\tilde{{\boldsymbol{x}}}={\boldsymbol{\mathcal{W}}}{\text{} }_{i-1}{{\boldsymbol{z}}}_{i-1}$
    下载: 导出CSV

    表  1  信道估计/均衡算法复杂度对比

    算法时间复杂度
    信道估计BEM-LS $ O\left( {{M^2}{N^2}\left( {QL} \right)} \right) $
    BEM-LMMSE $ O\left( {{M^2}{N^2}\left( {QL} \right)} \right) $
    BEM-SBL $ O\left( {{I_S}{M^2}{N^2}\left( {QL} \right)} \right) $
    信道均衡ZF $ O\left( {{M^3}{N^3}} \right) $
    LMMSE $ O\left( {{M^3}{N^3}} \right) $
    d-LMSR $ O\left( {2{M^2}{N^2} + I_K^3} \right) $
    下载: 导出CSV

    表  2  仿真系统参数

    参数名称参数值
    子载波个数($M$)32
    符号个数($N$)16
    载波频率5.9 GHz
    调制方式QPSK/16QAM
    用户移动速度121.5~607.5 km/h
    子载波间隔15 kHz
    信道模型EVA[20]
    CP长度7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-06-25
  • 修回日期:  2023-09-12
  • 网络出版日期:  2023-09-15
  • 刊出日期:  2024-06-30

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