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Deep learning has recently been successfully applied in automatic modulationclassification by extracting and classifying radio features in an end-to-endway. However, deep learning-based radio modulation classifiers are lack ofinterpretability, and there is little explanation or visibility into what kindsof radio features are extracted and chosen for classification. In this paper,we visualize different deep learning-based radio modulation classifiers byintroducing a class activation vector. Specifically, both convolutional neuralnetworks (CNN) based classifier and long short-term memory (LSTM) basedclassifier are separately studied, and their extracted radio features arevisualized. Extensive numerical results show both the CNN-based classifier andLSTM-based classifier extract similar radio features relating to modulationreference points. In particular, for the LSTM-based classifier, its obtainedradio features are similar to the knowledge of human experts. Our numericalresults indicate the radio features extracted by deep learning-basedclassifiers greatly depend on the contents carried by radio signals, and ashort radio sample may lead to misclassification.

Visualizing Deep Learning-based Radio Modulation Classifier