TY - JOUR
T1 - A Survey of Modulation Classification Using Deep Learning
T2 - Signal Representation and Data Preprocessing
AU - Peng, Shengliang
AU - Sun, Shujun
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - 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.
AB - 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.
KW - Deep learning (DL)
KW - feature representation
KW - image representation
KW - modulation classification
KW - sequence representation
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U2 - 10.1109/TNNLS.2021.3085433
DO - 10.1109/TNNLS.2021.3085433
M3 - Article
C2 - 34125689
AN - SCOPUS:85112221597
SN - 2162-237X
VL - 33
SP - 7020
EP - 7038
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 12
ER -