Interpretability of Modulation Recognition Network Based on Time-Frequency Gradient-Weighted Class Activation Mapping
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 learningGrad-CAMmodulation recognitiontime frequency analysis
国家科技期刊平台
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