Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram
In signal communication based on a non-cooperative communication system, the receiver is an unlicensed third-party communication terminal, and the modulation parameters of the transmitter signal cannot be predicted in advance. After the RF signal passes through the RF band-pass filter, low noise amp...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/3/552 |
| _version_ | 1797624855627038720 |
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| author | Hui Han Zhijian Yi Zhigang Zhu Lin Li Shuaige Gong Bin Li Mingjie Wang |
| author_facet | Hui Han Zhijian Yi Zhigang Zhu Lin Li Shuaige Gong Bin Li Mingjie Wang |
| author_sort | Hui Han |
| collection | DOAJ |
| description | In signal communication based on a non-cooperative communication system, the receiver is an unlicensed third-party communication terminal, and the modulation parameters of the transmitter signal cannot be predicted in advance. After the RF signal passes through the RF band-pass filter, low noise amplifier, and image rejection filter, the intermediate frequency signal is obtained by down-conversion, and then the IQ signal is obtained in the baseband by using the intermediate frequency band-pass filter and down-conversion. In this process, noise and signal frequency offset are inevitably introduced. As the basis of subsequent analysis and interpretation, modulation recognition has important research value in this environment. The introduction of deep learning also brings new feature mining tools. Based on this, this paper proposes a signal modulation recognition method based on multi-feature fusion and constructs a deep learning network with a double-branch structure to extract the features of IQ signal and multi-channel constellation, respectively. It is found that through the complementary characteristics of different forms of signals, a more complete signal feature representation can be constructed. At the same time, it can better alleviate the influence of noise and frequency offset on recognition performance, and effectively improve the classification accuracy of modulation recognition. |
| first_indexed | 2024-03-11T09:48:37Z |
| format | Article |
| id | doaj.art-eeeffbe0885347578b6eeaef3b0acce2 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T09:48:37Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-eeeffbe0885347578b6eeaef3b0acce22023-11-16T16:28:05ZengMDPI AGElectronics2079-92922023-01-0112355210.3390/electronics12030552Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation DiagramHui Han0Zhijian Yi1Zhigang Zhu2Lin Li3Shuaige Gong4Bin Li5Mingjie Wang6State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, 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, ChinaState Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, ChinaXi’an Satellite Control Center, Xi’an 710043, ChinaAcademy For Network and Communications of CETC, Shijiazhuang 050081, ChinaIn signal communication based on a non-cooperative communication system, the receiver is an unlicensed third-party communication terminal, and the modulation parameters of the transmitter signal cannot be predicted in advance. After the RF signal passes through the RF band-pass filter, low noise amplifier, and image rejection filter, the intermediate frequency signal is obtained by down-conversion, and then the IQ signal is obtained in the baseband by using the intermediate frequency band-pass filter and down-conversion. In this process, noise and signal frequency offset are inevitably introduced. As the basis of subsequent analysis and interpretation, modulation recognition has important research value in this environment. The introduction of deep learning also brings new feature mining tools. Based on this, this paper proposes a signal modulation recognition method based on multi-feature fusion and constructs a deep learning network with a double-branch structure to extract the features of IQ signal and multi-channel constellation, respectively. It is found that through the complementary characteristics of different forms of signals, a more complete signal feature representation can be constructed. At the same time, it can better alleviate the influence of noise and frequency offset on recognition performance, and effectively improve the classification accuracy of modulation recognition.https://www.mdpi.com/2079-9292/12/3/552constellation diagramIQ featuremodulation recognitionmulti-feature fusion |
| spellingShingle | Hui Han Zhijian Yi Zhigang Zhu Lin Li Shuaige Gong Bin Li Mingjie Wang Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram Electronics constellation diagram IQ feature modulation recognition multi-feature fusion |
| title | Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram |
| title_full | Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram |
| title_fullStr | Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram |
| title_full_unstemmed | Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram |
| title_short | Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram |
| title_sort | automatic modulation recognition based on deep learning features fusion of signal and constellation diagram |
| topic | constellation diagram IQ feature modulation recognition multi-feature fusion |
| url | https://www.mdpi.com/2079-9292/12/3/552 |
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