Visualizing Deep Learning-Based Radio Modulation Classifier

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: IEEE Transactions on Cognitive Communications and Networking

سال: 2021

ISSN: ['2332-7731', '2372-2045']

DOI: https://doi.org/10.1109/tccn.2020.3048113