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Radio Signal Automatic Modulation Classification based on Deep Learning and Expert Features

机译：基于深度学习和专家特征的无线电信号自动调制分类

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摘要

Automatic modulation classification (AMC) becomes more and more important in the electronic reconnaissance. Recently, lots of researchers focus on deep learning architecture based AMC approach but the recognition rate of WBFM and QAM is less than desirable. In this paper, we proposed a joint AMC model of two expert features and CNN-LSTM networks. Before entering the deep learning network, the un-classified signal is first detected whether WBFM or not by the maximum of zero-center normalization amplitude spectrum density. Then the signal which is not WBFM will be inputted to the CNN-LSTM network, while QAM16 and QAM64 are regarded as the same class here. Finally, Haar-wavelet transform crest searching is used to classify QAM16 and QAM64. Compared with former CNN-LSTM architecture, the results of the experiment and deduction show the average recognition rate of the proposed model is increased by 11.5% at 10 dB SNR.
机译：在电子侦察中，自动调制分类（AMC）变得越来越重要。最近，许多研究人员将注意力集中在基于深度学习架构的AMC方法上，但是WBFM和QAM的识别率并不理想。在本文中，我们提出了具有两个专家功能和CNN-LSTM网络的联合AMC模型。在进入深度学习网络之前，首先通过零中心归一化幅度谱密度的最大值来检测未分类信号是否是WBFM。然后将不是WBFM的信号输入到CNN-LSTM网络，而QAM16和QAM64在这里被视为同一类。最后，使用Haar小波变换波峰搜索对QAM16和QAM64进行分类。与以前的CNN-LSTM架构相比，实验结果和推论结果表明，在SNR为10 dB时，该模型的平均识别率提高了11.5％。

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