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基于快速贝叶斯匹配追踪优化的海上稀疏信道估计方法

张颖 姚雨丰

张颖, 姚雨丰. 基于快速贝叶斯匹配追踪优化的海上稀疏信道估计方法[J]. 电子与信息学报, 2020, 42(2): 534-540. doi: 10.11999/JEIT190102
引用本文: 张颖, 姚雨丰. 基于快速贝叶斯匹配追踪优化的海上稀疏信道估计方法[J]. 电子与信息学报, 2020, 42(2): 534-540. doi: 10.11999/JEIT190102
Ying ZHANG, Yufeng YAO. Channel Estimation Algorithm of Maritime Sparse Channel Based on Fast Bayesian Matching Pursuit Optimization[J]. Journal of Electronics and Information Technology, 2020, 42(2): 534-540. doi: 10.11999/JEIT190102
Citation: Ying ZHANG, Yufeng YAO. Channel Estimation Algorithm of Maritime Sparse Channel Based on Fast Bayesian Matching Pursuit Optimization[J]. Journal of Electronics and Information Technology, 2020, 42(2): 534-540. doi: 10.11999/JEIT190102

基于快速贝叶斯匹配追踪优化的海上稀疏信道估计方法

doi: 10.11999/JEIT190102
基金项目: 国家自然科学基金(61673259)
详细信息
    作者简介:

    张颖:男,1968年生,博士,教授,博士生导师,研究方向为物联网、海事无线通信、无线自组织网络

    姚雨丰:男,1995年生,硕士生,研究方向为海事无线通信信道估计、无线信号传输技术

    通讯作者:

    张颖 yingzhang@shmtu.edu.cn

  • 中图分类号: TN911

Channel Estimation Algorithm of Maritime Sparse Channel Based on Fast Bayesian Matching Pursuit Optimization

Funds: The National Natural Science Foundation of China (61673259)
  • 摘要: 正交频分复用(OFDM)系统中,由于频率发生选择性衰落会导致信道在数据传输中产生符号间干扰,因此接收机往往需要知道信道状态信息。而在海上通信的情况下,信道传输会受到多种外界因素的干扰,往往需要预先进行信道探测估计。为了提高估计性能,该文提出一种基于奇异值分解优化观测矩阵的快速贝叶斯匹配追踪稀疏信道估计优化算法(FBMPO),该算法不仅能够充分考虑海上通信的信道稀疏性,也能够降低信道的不确定性带来的影响。计算机仿真实验表明,与传统的信道估计算法相比,该算法能够提高信道估计的精确度。
  • 图  1  海上通信损耗模型

    图  2  N为32时,p1为0.04时,3种算法的AMSE对比

    图  4  N为64时,p1为0.04时,3种算法的AMSE对比

    图  5  N为32时,p1为0.01时,3种算法的AMSE对比

    图  3  N为48时,p1为0.04时,3种算法的AMSE对比

    图  6  N为32时,p1为0.04时,3种算法的BER对比

    图  8  N为64时,p1为0.04时,3种算法的BER对比

    图  9  N为32时,p1为0.01时,3种算法的BER对比

    图  7  N为48时,p1为0.04时,3种算法的BER对比

    表  1  FBMPO算法的伪代码

     FBMPO算法
     输入:参数向量s, 观测矩阵${{\varphi } }_i$,迭代阈值K, R and L
     输出:${\tilde h_{ {\rm{MMSE} } } }$;
        (1) Initialize ${\mu _{0,1}}$ by式(20)
        (2) for i ← 1 to L:
        (3)   ${{{b}}_i} \leftarrow {{{\varphi}} ^{ - 1}}{{{\phi}} _i};\;{{{\beta }}_i} \leftarrow {\left( {1 + {\sigma _1}^2{{\phi}} _i^{\rm{T}}{{{b}}_i}} \right)^{ - 1}}$;
        (4)   ${\mu _{1,i} }^* \leftarrow {\mu _{0,1} } + \dfrac{1}{2}\lg \left( {\frac{ { { {{\beta} } _i} } }{ { {\sigma _1}^2} } } \right) + \dfrac{1}{2}{ {{\beta} } _i}{\left| { { {{y} }^{\rm{T} } }{ {{b} }_i} } \right|^2}$
              $ + {\rm{lg} }\dfrac{ { {p_1} } }{ {1 - {p_1} } }$;
        (5) end for
        (6) for q ← 1 to K:
        (7)   ${\mu _{1,q}} \leftarrow {\mu _{1,i}}^*$; ${\rm{}}{b_{1,q}}^{\left( 1 \right)} \leftarrow {\mu _{1,i}}^*$; ${\rm{}}{c_{1,q}}^{\left( 1 \right)} \leftarrow {c_{1,i}}^*$;
            ${\beta _{1,q}}^{\left( 1 \right)} \leftarrow {\beta _{1,i}}^*$;
        (8) end for
        (9) ${{{\phi}}_i} \leftarrow {{{U}}_1} {{W}_2} {{{V}}_1}^{\rm T}$; ${{{\phi}} _i}' \leftarrow {{{U}}_1}{{{W}}_2}'{{{V}}_1}^{\rm{T}}$;
        (10) for l ← 1 to R:
        (11)   ${{{\beta}} _i} \leftarrow {\left( {1 + {\sigma _1}^2{{{\phi}} _i}{{'}^{\rm{T}}}{{{b}}_i}} \right)^{ - 1}}$;
        (12)   ${{{\mu}} _i} \leftarrow {\mu ^{\left( {l - 1} \right)}} + \dfrac{1}{2}{\rm{lg}}{{{\beta}} _i} + \dfrac{1}{2}{{{\beta}} _i}{\left( {{{{s}}^{\rm{T}}}c_i^{\left( l \right)}} \right)^2} $
            $ + {\rm{lg}}\frac{{{p_1}}}{{1 - {p_1}}}$;
        (13)   $i_*^{\left( l \right)} \leftarrow {\rm{argma}}{{\rm{x}}_i}{\mu _i}$;
        (14)   ${G^{\left( l \right)}} \leftarrow {G^{\left( {l - 1} \right)}} \cup ^{\{i_{*}^{(l)}\}} $;
            $c_i^{\left( {l + 1} \right)} \leftarrow c_i^{\left( l \right)} - {{i}}_{i_*^{\left( l \right)}}^{\left( l \right)}{{{\beta }}_{i_*^{\left( l \right)}}}{{i}}_{i_*^{\left( l \right)}}^{{{\left( l \right)}^{\rm{T}}}}{{{\phi}} _i}$;
        (15) end for
        (16) 计算${\tilde h_{ {\rm{MMSE} } } }$ by式(30)
    下载: 导出CSV

    表  2  系统仿真参数设置

    参数仿真参数值
    信道抽头数系统信道带宽6410 MHz
    采样频率循环前缀长度10 MHz16
    调制方式BPSK
    非零抽头概率 p1{0.04,0.01}
    FFT/IFFT点数1024
    训练序列长度{32,48,64}
    下载: 导出CSV

    表  3  不同算法在不同训练序列时的运算时间(s)

    N=32N=48N=64
    OMP6.42848.041311.4591
    BCS18.254120.893124.5212
    FBMPO11.461813.719415.0951
    下载: 导出CSV
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    出版历程
    • 收稿日期:  2019-02-21
    • 修回日期:  2019-09-01
    • 网络出版日期:  2019-09-06
    • 刊出日期:  2020-02-19

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