A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification
Automatic modulation recognition is a necessary part of cooperative and noncooperative communication systems and plays an important role in military and civilian fields. Although the constellation diagram (CD) is an essential feature for different digital modulations, it is hard to be extracted unde...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/4/920 |
| _version_ | 1797621296591273984 |
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| author | Pengfei Ma Yuesen Liu Lin Li Zhigang Zhu Bin Li |
| author_facet | Pengfei Ma Yuesen Liu Lin Li Zhigang Zhu Bin Li |
| author_sort | Pengfei Ma |
| collection | DOAJ |
| description | Automatic modulation recognition is a necessary part of cooperative and noncooperative communication systems and plays an important role in military and civilian fields. Although the constellation diagram (CD) is an essential feature for different digital modulations, it is hard to be extracted under noncooperative complex communication environment. Frequency offset, especially the nonlinear frequency offset is a vital problem of complex communication environment, which greatly affects the extraction of traditional CD and the performance of modulation recognition methods. In the current paper, we propose an antifrequency offset constellation diagram (AFO-CD) extraction method, which combines the constellation diagram with a convolutional neural network (CNN). The proposed method indicates the change of the CD with time and enables us to suppress the influence of frequency offset efficiently. Additionally, a residual units-based classifier is designed for multiscale feature extraction and modulation classification. The experimental results demonstrate that the proposed method can effectively improve the recognition accuracy and has a good application prospect in the complex electromagnetic environment. |
| first_indexed | 2024-03-11T08:54:56Z |
| format | Article |
| id | doaj.art-61ed97b3ead34b18bf75a2793b933e32 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T08:54:56Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-61ed97b3ead34b18bf75a2793b933e322023-11-16T20:12:02ZengMDPI AGElectronics2079-92922023-02-0112492010.3390/electronics12040920A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation ClassificationPengfei Ma0Yuesen Liu1Lin Li2Zhigang Zhu3Bin Li4The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, ChinaSchool of Electronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Electronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Electronic Engineering, Xidian University, Xi’an 710071, ChinaXi’an Satellite Control Center, Xi’an 710043, ChinaAutomatic modulation recognition is a necessary part of cooperative and noncooperative communication systems and plays an important role in military and civilian fields. Although the constellation diagram (CD) is an essential feature for different digital modulations, it is hard to be extracted under noncooperative complex communication environment. Frequency offset, especially the nonlinear frequency offset is a vital problem of complex communication environment, which greatly affects the extraction of traditional CD and the performance of modulation recognition methods. In the current paper, we propose an antifrequency offset constellation diagram (AFO-CD) extraction method, which combines the constellation diagram with a convolutional neural network (CNN). The proposed method indicates the change of the CD with time and enables us to suppress the influence of frequency offset efficiently. Additionally, a residual units-based classifier is designed for multiscale feature extraction and modulation classification. The experimental results demonstrate that the proposed method can effectively improve the recognition accuracy and has a good application prospect in the complex electromagnetic environment.https://www.mdpi.com/2079-9292/12/4/920automatic modulation recognitionconvolutional neural networkconstellation diagramfrequency offset |
| spellingShingle | Pengfei Ma Yuesen Liu Lin Li Zhigang Zhu Bin Li A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification Electronics automatic modulation recognition convolutional neural network constellation diagram frequency offset |
| title | A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification |
| title_full | A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification |
| title_fullStr | A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification |
| title_full_unstemmed | A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification |
| title_short | A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification |
| title_sort | robust constellation diagram representation for communication signal and automatic modulation classification |
| topic | automatic modulation recognition convolutional neural network constellation diagram frequency offset |
| url | https://www.mdpi.com/2079-9292/12/4/920 |
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