A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing

Modulation classification is one of the key tasks for communications systems monitoring, management, and control for addressing technical issues, including spectrum awareness, adaptive transmissions, and interference avoidance. Recently, deep learning (DL)-based modulation classification has attracted significant attention due to its superiority in feature extraction and classification accuracy. In DL-based modulation classification, one major challenge is to preprocess a received signal and represent it in a proper format before feeding the signal into deep neural networks. This article provides a comprehensive survey of the state-of-the-art DL-based modulation classification algorithms, especially the techniques of signal representation and data preprocessing utilized in these algorithms. Since a received signal can be represented by either features, images, sequences, or a combination of them, existing algorithms of DL-based modulation classification can be categorized into four groups and are reviewed accordingly in this article. Furthermore, the advantages as well as disadvantages of each signal representation method are summarized and discussed.

Automated summary

This article provides a comprehensive survey of state-of-the-art deep learning-based modulation classification algorithms, focusing on the techniques of signal representation and data preprocessing utilized in these algorithms. The algorithms can be categorized into four groups based on how the received signal is represented, and the advantages as well as disadvantages of each representation method are summarized and discussed.