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基于生成式对抗网络和多模态注意力机制的扩频与常规调制信号识别方法

王华华 张睿哲 黄永洪

王华华, 张睿哲, 黄永洪. 基于生成式对抗网络和多模态注意力机制的扩频与常规调制信号识别方法[J]. 电子与信息学报, 2024, 46(4): 1212-1221. doi: 10.11999/JEIT230518
引用本文: 王华华, 张睿哲, 黄永洪. 基于生成式对抗网络和多模态注意力机制的扩频与常规调制信号识别方法[J]. 电子与信息学报, 2024, 46(4): 1212-1221. doi: 10.11999/JEIT230518
WANG Huahua, ZHANG Ruizhe, HUANG Yonghong. Spread Spectrum and Conventional Modulation Signal Recognition Method Based on Generative Adversarial Network and Multi-modal Attention Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1212-1221. doi: 10.11999/JEIT230518
Citation: WANG Huahua, ZHANG Ruizhe, HUANG Yonghong. Spread Spectrum and Conventional Modulation Signal Recognition Method Based on Generative Adversarial Network and Multi-modal Attention Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1212-1221. doi: 10.11999/JEIT230518

基于生成式对抗网络和多模态注意力机制的扩频与常规调制信号识别方法

doi: 10.11999/JEIT230518
基金项目: 国家自然科学基金(61701063),重庆市自然科学基金(cstc2021jcyj-msxmX0454)
详细信息
    作者简介:

    王华华:男,正高级工程师,研究方向为移动通信基带信号处理

    张睿哲:男,硕士生,研究方向为深度学习、无线信号识别

    黄永洪:男,副教授,研究方向为安全操作系统、人工智能安全

    通讯作者:

    张睿哲 1790678249@qq.com

  • 中图分类号: TN911.7

Spread Spectrum and Conventional Modulation Signal Recognition Method Based on Generative Adversarial Network and Multi-modal Attention Mechanism

Funds: The National Natural Science Foundation of China (61701063), The Natural Science Foundation of Chongqing (cstc2021jcyj-msxmX0454)
  • 摘要: 针对低信噪比条件下的扩频与常规调制信号分类精度低的问题,该文提出一种基于生成式对抗网络(GAN)、卷积神经网络(CNN)和长短期记忆(LSTM)网络的多模态注意力机制信号调制识别方法。首先生成待识别信号的时频图像(TFIs),并利用GAN实现TFIs降噪处理;然后将信号的同相正交数据(I/Q data)与TFIs作为模型输入,并搭建基于CNN的TFIs识别支路和基于LSTM的I/Q数据识别支路;最后,在模型中添加注意力机制,增强I/Q数据和TFIs中重要特征对分类结果的决定作用。实验结果表明,该文所提方法相较于单模态识别模型以及其它基线模型,整体分类精度有效提升2%~7%,并在低信噪比条件下具备更强的特征表达能力和鲁棒性。
  • 图  1  基于U-net的生成器

    图  2  基于全卷积网络的鉴别器

    图  3  CNN-Attention模型

    图  4  LSTM-Attention模型

    图  5  Channel-Attention模型

    图  6  GAN-MmACL模型多类识别准确率曲线

    图  7  消融实验结果

    图  8  在本文所提数据集上不同模型的分类精度

    图  9  SNR=–10 dB条件下,在文献[10]所提数据集上不同模型的分类精度

    图  10  SNR=–8 dB条件下,经过GAN降噪处理前后TFIs

    表  1  数据集相关参数

    参数 数值
    码元速率(kHz) 2
    采样率(kHz) 160
    载波频率(kHz) 40
    采样点数 960
    信号持续时间(ms) 12
    TFIs像素(RGB) (256,256)×3
    噪声环境 AWGN(–10:2:8 dB)
    FHSS频点(kHz) 26, 30, 34, 38, 42, 46, 50, 54
    下载: 导出CSV

    表  2  网络相关参数

    参数 数值
    学习率 0.001
    GAN网络中卷积核大小 4×4
    CNN-Attention层中卷积核大小 7×7, 5×5, 3×3
    LSTM-Attention层中卷积核大小 7×1, 5×1, 3×1
    LSTM单元数量 128
    丢弃率 0.5
    批次大小 32
    优化器 Adam
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-30
  • 修回日期:  2023-12-26
  • 网络出版日期:  2023-12-29
  • 刊出日期:  2024-04-24

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