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基于神经网络的OFDM信道补偿与信号检测

刘步花 丁丹 杨柳

刘步花, 丁丹, 杨柳等 . 基于神经网络的OFDM信道补偿与信号检测[J]. 北京航空航天大学学报, 2020, 46(7): 1363-1370. doi: 10.13700/j.bh.1001-5965.2019.0456
引用本文: 刘步花, 丁丹, 杨柳等 . 基于神经网络的OFDM信道补偿与信号检测[J]. 北京航空航天大学学报, 2020, 46(7): 1363-1370. doi: 10.13700/j.bh.1001-5965.2019.0456
LIU Buhua, DING Dan, YANG Liuet al. Channel compensation and signal detection of OFDM based on neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1363-1370. doi: 10.13700/j.bh.1001-5965.2019.0456(in Chinese)
Citation: LIU Buhua, DING Dan, YANG Liuet al. Channel compensation and signal detection of OFDM based on neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1363-1370. doi: 10.13700/j.bh.1001-5965.2019.0456(in Chinese)

基于神经网络的OFDM信道补偿与信号检测

doi: 10.13700/j.bh.1001-5965.2019.0456
基金项目: 

国家“863”计划 2015AA7026085

详细信息
    作者简介:

    刘步花  女, 硕士研究生。主要研究方向:深度学习在无线传输物理层的运用

    丁丹  男, 博士, 副研究员。主要研究方向:航天测控通信

    通讯作者:

    丁丹, E-mail: ddnjr@163.com

  • 中图分类号: TN914.34

Channel compensation and signal detection of OFDM based on neural network

Funds: 

National High-tech Research and Development Program of China 2015AA7026085

More Information
  • 摘要:

    针对非线性失真和多径效应混合的复杂信道条件,提出一种基于神经网络的正交频分复用(OFDM)信道补偿与信号检测的方法。首先接收端信号利用最小二乘(LS)算法和迫零(ZF)算法做预处理,然后再输入到一层全链接层的神经网络进行进一步的信道补偿与信号检测,并恢复数据流。仿真结果表明,在没有进行输入信号功率回退(IBO)时,所提方法的误比特率(BER)性能比LS算法提升2个数量级,比线性最小均方误差(LMMSE)、最小均方误差(MMSE)提升一个数量级;在进行IBO后,所提方法能避免LS信道估计下至少4 dB的功率损失,能避免LMMSE、MMSE信道估计下至少2 dB的功率损失。所提方法在一定程度上验证了机器学习结合通信的先验知识的这种新的网络结构更能提升系统数据传输的准确率。

     

  • 图 1  非线性和多径影响下的OFDM系统

    Figure 1.  OFDM system under influence of nonlinearity and multi-path

    图 2  神经网络接收机结构

    Figure 2.  Neural network receiver architecture

    图 3  发送端OFDM帧结构

    Figure 3.  OFDM frame structure at transmitter

    图 4  数据产生流程

    Figure 4.  Flowchart of data generation

    图 5  AM-AM和AM-PM非线性放大

    Figure 5.  AM-AM and AM-PM nonlinear amplification

    图 6  LSZF-Net与无非线性补偿的传统方法比较

    Figure 6.  Comparison between LSZF-Net and traditional methods without non-linear compensation

    图 7  LSZF-Net与进行IBO的传统方法比较

    Figure 7.  Comparison between LSZF-Net and traditional method after IBO

    图 8  不同时延扩展的多径信道LSZF-Net性能对比

    Figure 8.  Performance comparison of LSZF-Net in different delay spread multi-path channel

    图 9  不同网络结构性能对比

    Figure 9.  Performance comparison of different network structures

    表  1  训练参数设置

    Table  1.   Training parameter setting

    OFDM参数 数值
    CP长度 16
    导频长度 64
    信噪比/dB 0:5:25
    epoch 3 000
    初始学习率 0.001
    注:OFDM帧结构为导频+数据;调制方式为QPSK; LSZF-Net无隐藏层;激活函数为tanh;优化器为rmsprop;损失函数为L2。
    下载: 导出CSV

    表  2  多径信道条件

    Table  2.   Multi-path channel conditions

    τrms 信道条件
    0.3Ts h=0.544 6+1.097 5i, 0.102 9+0.207 3i, 0.019 4+0.039 2i, 0.003 7+0.007 4i
    0.5Ts h=0.821 8+0.542 5i, 0.302 3+0.199 6i, 0.111 2+0.073 4i, 0.040 9+0.027 0i, 0.015 1+0.009 9i, 0.005 5+0.003 7i
    0.7Ts h=0.525 6-0.577 8i, 0.257 3-0.282 9i, 0.126 0-0.138 5i, 0.061 7-0.067 8i, 0.030 2+0.033 2i, 0.014 8-0.016 2i, 0.007 2-008 0i, 0.003 5-0.003 9i
    下载: 导出CSV

    表  3  不同方法计算复杂度比较

    Table  3.   Comparison of computational complexity among different methods

    方法 所需乘积的次数
    LS 2N
    MMSE >2N2
    LMMSE 2N2
    LSZF-Net (4N)2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-08-26
  • 录用日期:  2020-02-21
  • 刊出日期:  2020-07-20

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