基于时频Grad-CAM的调制识别网络可解释分析

梁先明; 倪帆; 陈文洁; 张家树

doi:10.3969/j.issn.0258-2724.20210791

基于时频Grad-CAM的调制识别网络可解释分析

doi: 10.3969/j.issn.0258-2724.20210791

1.
中国电子科技集团公司第十研究所，四川成都，610036
2.
西南交通大学信息科学与技术学院，四川成都，610031
3.
西南交通大学计算机与人工智能学院，四川成都，610031

基金项目: 国家自然科学基金项目（62071396）；四川省自然科学基金项目（2022NSFSC0531）

详细信息

作者简介:
梁先明（1976—），男，高级工程师，研究方向为通信信号处理与分析、智能信号分析，E-mail：liangxm8873@126.com

通讯作者:
张家树（1965—），男，教授，博士，研究方向为神经网络与机器智能、信号与信息智能处理、信息安全技术，E-mail：jszhang@swjtu.edu.cn

中图分类号: TP301.6
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- 文章访问数: 253
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- 被引次数: 0
出版历程
- 收稿日期: 2021-09-10
- 修回日期: 2022-05-16
- 网络出版日期: 2024-06-14
- 刊出日期: 2022-06-09

Interpretability of Modulation Recognition Network Based on Time-Frequency Gradient-Weighted Class Activation Mapping

1.
The 10th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China
2.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China
3.
School of Computer and Artificial Intelligence, Southwest Jiaotong University, Chengdu 610031, China

摘要

摘要:
针对时频深度学习调制识别方法存在可解释性差的问题，提出一种基于时频梯度加权类激活映射（Grad-CAM）的调制识别网络可解释框架. 该框架通过时频Grad-CAM可视化深度模型中隐含层的关键特征，从视觉上解释网络隐含层提取的时频深度特征对于正确与错误识别中的作用，揭示低信噪比环境下网络性能下降的内在机理，并通过量化和排序网络中每层不同卷积核的贡献值来判断网络的冗余程度. 仿真实验结果验证了基于时频Grad-CAM的调制识别网络可解释性框架的有效性；可解释分析结果表明，在低信噪比环境下，网络特征提取区域有大量噪声存在，且本文所测试的调制识别网络冗余程度较为严重.
- 可解释深度学习 /
- 梯度类加权激活映射 /
- 调制识别 /
- 时频分析
Abstract:
Aiming at the poor interpretability of modulation recognition methods based on time-frequency deep learning, an interpretable framework of a modulation recognition network is proposed, utilizing time-frequency gradient-weighted class activation mapping (Grad-CAM). Through the key features of the hidden layer in the Grad-CAM visual deep model, the significance of the deep features extracted from the network hidden layer are illustrated in terms of correct and error recognition, revealing the decline of network performance in the environment of low signal-to-noise ratio (SNR). The contribution values of different convolution cores at each network layer are quantified and sorted to determine the network redundancy. The simulation results verify the interpretable framework of the time-frequency deep learning network for modulation recognition. The interpretable analysis results reflect that there is a large amount of noise present in the feature extraction region of the network in a low signal-to-noise ratio environment, and the tested modulation recognition network exhibits a high degree of redundancy.
- interpretable deep learning /
- Grad-CAM /
- modulation recognition /
- time frequency analysis

HTML全文

图 1 可解释调制识别网络设计

Figure 1. Design of interpretable modulation recognition network

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图 2 12种调制信号的二维时频特征图

Figure 2. 2D time-frequency characteristic diagrams of 12 modulation signals

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图 3 SE-ResNet50结构

Figure 3. SE-ResNet50 structure

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图 4 二维时频特征提取的调制识别整体准确率

Figure 4. Overall accuracy of modulation recognition based on 2D time-frequency feature extraction

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图 5 0 dB下SE-ResNet识别结果的混淆矩阵

Figure 5. Confusion matrix of recognition results with SE-ResNet at 0 dB

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图 6 −10 dB下SE-ResNet识别结果的混淆矩阵

Figure 6. Confusion matrix of recognition results with SE-Res Net at −10 dB

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图 7 类激活图在不同时频特征上的映射结果

Figure 7. Mapping results of class activation graphs on different time-frequency features

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图 8 4条BPSK信号时频图在SE-ResNet下的梯度类激活映射

Figure 8. Gradient-weighted class activation mapping of time-frequency graphs from four BPSK modulated signals with SE-ResNet

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图 9 每类信号时频特征对于不同预测类别的梯度类激活映射

Figure 9. Gradient class activation mapping of time-frequency characteristics from each class of signals for different prediction classes

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图 10 8PSK调制信号的时频特征在不同信噪比下的SE-ResNet50隐藏层最后一层的类激活图

Figure 10. Class activation graphs of time-frequency characteristics of 8PSK modulated signals on the last hidden layers with SE-ResNet50 under different SNR ratios

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图 11 SE-ResNet、ResNet50和VGGNet对于同种调制信号时频特征的类激活图

Figure 11. Class activation graphs of SE-ResNet, ResNet50 and VGGNet for the same modulated signal

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图 12 4种深度网络最后一层的卷积核贡献值排序

Figure 12. Ranking of kernel contribution values on the last convolution layer of four deep networks

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图 13 VGG16最后一层卷积层每个卷积核对应的贡献值

Figure 13. Contribution values of each kernel on the last convolution layer of VGG16

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