期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 33, 期 12, 页码 7020-7038出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3085433
关键词
Modulation; Feature extraction; Signal representation; Data preprocessing; Task analysis; Deep learning; Binary phase shift keying; Deep learning (DL); feature representation; image representation; modulation classification; sequence representation
类别
资金
- Fundamental Research Funds for the Central Universities [ZQN-708]
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.
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.
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