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基于调制卷积神经网络的空地数据链信道估计

刘春辉 王美琳 董赞亮 王沛

刘春辉, 王美琳, 董赞亮, 等 . 基于调制卷积神经网络的空地数据链信道估计[J]. 北京航空航天大学学报, 2022, 48(3): 533-543. doi: 10.13700/j.bh.1001-5965.2020.0591
引用本文: 刘春辉, 王美琳, 董赞亮, 等 . 基于调制卷积神经网络的空地数据链信道估计[J]. 北京航空航天大学学报, 2022, 48(3): 533-543. doi: 10.13700/j.bh.1001-5965.2020.0591
LIU Chunhui, WANG Meilin, DONG Zanliang, et al. Channel estimation of air-ground data link based on modulated convolutional neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(3): 533-543. doi: 10.13700/j.bh.1001-5965.2020.0591(in Chinese)
Citation: LIU Chunhui, WANG Meilin, DONG Zanliang, et al. Channel estimation of air-ground data link based on modulated convolutional neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(3): 533-543. doi: 10.13700/j.bh.1001-5965.2020.0591(in Chinese)

基于调制卷积神经网络的空地数据链信道估计

doi: 10.13700/j.bh.1001-5965.2020.0591
基金项目: 

科技创新2030-“新一代人工智能”重大项目 2020AAA0108200

北京市自然科学基金 4204102

详细信息
    通讯作者:

    刘春辉, E-mail: liuchunhui2134@buaa.edu.cn

  • 中图分类号: V243.5; TN911.72

Channel estimation of air-ground data link based on modulated convolutional neural network

Funds: 

Science and Technology Innovation 2030-Key Project of "New Generation Artificial Intelligence" 2020AAA0108200

Beijing Municipal Natural Science Foundation 4204102

More Information
  • 摘要:

    针对复杂环境下空地数据链正交频分复用(OFDM)系统信道估计精度不足的问题,提出了一种基于调制卷积神经网络(MCNN)和双向长短时记忆网络(BiLSTM)结合的信道估计算法。利用最小二乘算法(LS)提取初始信道状态信息(CSI);利用MCNN网络提取初始CSI的深度特征,并对网络模型进行压缩;利用BiLSTM网络对最终CSI进行预测,实现信道估计。利用构建的空地信道模型生成信道系数数据集,实现神经网络模型的训练与测试。仿真结果表明:与传统算法和现有深度学习方法相比,所提出的信道估计算法具有更小的估计误差,高信噪比条件下的系统误码率(BER)性能提升接近一个数量级;由于引入了调制滤波器技术,随着神经网络层数增加,网络模型参数量大幅减少。

     

  • 图 1  基于OFDM的通信系统结构

    Figure 1.  Communication system structure based on OFDM

    图 2  二径空地信道模型

    Figure 2.  Two-ray air-ground channel model

    图 3  基于深度学习的信道估计框架

    Figure 3.  Channel estimation framework based on deep learning

    图 4  基于调制滤波器的调制过程

    Figure 4.  Modulation process based on modulation filter

    图 5  BiLSMT网络结构

    Figure 5.  BiLSTM network structure

    图 6  MC-BI网络模型的训练与应用

    Figure 6.  Training and application of MC-BI network model

    图 7  三种典型场景下不同信道估计算法的NMSE比较

    Figure 7.  NMSE comparison of different channel estimation algorithms in three typical scenes

    图 8  三种典型场景下不同信道估计算法的BER曲线

    Figure 8.  BER curves of different channel estimation algorithms in three typical scenes

    图 9  压缩前后模型参数量比较

    Figure 9.  Comparison of model parameters before and after compression

    图 10  MCNN+BiLSTM与CNN+BiLSTM网络BER对比

    Figure 10.  BER comparison between MCNN+BiLSTM network and CNN+BiLSTM network

    图 11  不同BiLSTM层与不同MCNN层结合的MC-BI算法的BER曲线

    Figure 11.  BER curves of MC-BI algorithm with different BiLSTM layers and MCNN layers

    图 12  三种MC-BI网络的BER比较

    Figure 12.  BER comparison of three MC-BI networks

    表  1  信道模型参数

    Table  1.   Channel model parameters

    场景 hA/m hG/m d/km n1 n2 sg/m
    郊区 1 000 20 46 15 1 0.05
    山区 4 000 20 60 5 1 1 000
    海上 800 20 25 81 1 0.187 2
    下载: 导出CSV

    表  2  OFDM系统参数设置

    Table  2.   OFDM system parameter setup

    参数 数值
    子载波数 64
    OFDM符号数 64
    CP长度 8
    导频间隔 8
    调制方式 4QAM
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-10-19
  • 录用日期:  2021-01-17
  • 刊出日期:  2022-03-20

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