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 attract...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 33; no. 12; pp. 7020 - 7038 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2162-237X 2162-2388 2162-2388 |
| DOI | 10.1109/TNNLS.2021.3085433 |
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| Abstract | 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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| AbstractList | 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.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. 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. |
| Author | Yao, Yu-Dong Peng, Shengliang Sun, Shujun |
| Author_xml | – sequence: 1 givenname: Shengliang orcidid: 0000-0001-6837-1754 surname: Peng fullname: Peng, Shengliang organization: College of Information Science and Technology, Huaqiao University, Xiamen, China – sequence: 2 givenname: Shujun orcidid: 0000-0003-4086-4834 surname: Sun fullname: Sun, Shujun organization: College of Information Science and Technology, Huaqiao University, Xiamen, China – sequence: 3 givenname: Yu-Dong orcidid: 0000-0003-3868-0593 surname: Yao fullname: Yao, Yu-Dong email: yyao@stevens.edu organization: Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34125689$$D View this record in MEDLINE/PubMed |
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| Snippet | Modulation classification is one of the key tasks for communications systems monitoring, management, and control for addressing technical issues, including... |
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| SubjectTerms | Adaptive control Algorithms Artificial neural networks Attention Avoidance learning Binary phase shift keying Classification Communications systems Data preprocessing Deep Learning Deep learning (DL) Feature extraction feature representation image representation Machine learning Modulation modulation classification Neural networks Neural Networks, Computer Preprocessing Representations sequence representation Signal representation Surveys Task analysis |
| Title | A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing |
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