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

Machine Learning;Signal Processing