A Review of Research on Signal Modulation Recognition Based on Deep Learning
Since the emergence of 5G technology, the wireless communication system has had a huge data throughput, so the joint development of artificial intelligence technology and wireless communication technology is one of the current mainstream development directions. In particular the combination of deep...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/17/2764 |
| _version_ | 1797495775677120512 |
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| author | Wenshi Xiao Zhongqiang Luo Qian Hu |
| author_facet | Wenshi Xiao Zhongqiang Luo Qian Hu |
| author_sort | Wenshi Xiao |
| collection | DOAJ |
| description | Since the emergence of 5G technology, the wireless communication system has had a huge data throughput, so the joint development of artificial intelligence technology and wireless communication technology is one of the current mainstream development directions. In particular the combination of deep learning technology and communication physical layer technology is the future research hotspot. The purpose of this research paper is to summarize the related algorithms of the combination of Automatic Modulation Recognition (AMR) technology and deep learning technology in the communication physical layer. In order to elicit the advantages of the modulation recognition algorithm based on deep learning, this paper firstly introduces the traditional AMR method, and then summarizes the advantages and disadvantages of the traditional algorithm. Then, the application of the deep learning algorithm in AMR is described, and the identification method based on a typical deep learning network is emphatically described. Afterwards, the existing Deep Learning (DL) modulation identification algorithm in a small sample environment is summarized. Finally, DL modulation is discussed, identifying field challenges, and future research directions. |
| first_indexed | 2024-03-10T01:54:28Z |
| format | Article |
| id | doaj.art-e0724e66c38440e0be699917f16b6af1 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T01:54:28Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e0724e66c38440e0be699917f16b6af12023-11-23T12:59:36ZengMDPI AGElectronics2079-92922022-09-011117276410.3390/electronics11172764A Review of Research on Signal Modulation Recognition Based on Deep LearningWenshi Xiao0Zhongqiang Luo1Qian Hu2School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, ChinaSince the emergence of 5G technology, the wireless communication system has had a huge data throughput, so the joint development of artificial intelligence technology and wireless communication technology is one of the current mainstream development directions. In particular the combination of deep learning technology and communication physical layer technology is the future research hotspot. The purpose of this research paper is to summarize the related algorithms of the combination of Automatic Modulation Recognition (AMR) technology and deep learning technology in the communication physical layer. In order to elicit the advantages of the modulation recognition algorithm based on deep learning, this paper firstly introduces the traditional AMR method, and then summarizes the advantages and disadvantages of the traditional algorithm. Then, the application of the deep learning algorithm in AMR is described, and the identification method based on a typical deep learning network is emphatically described. Afterwards, the existing Deep Learning (DL) modulation identification algorithm in a small sample environment is summarized. Finally, DL modulation is discussed, identifying field challenges, and future research directions.https://www.mdpi.com/2079-9292/11/17/2764automatic modulation recognitiondeep learningwireless communicationintelligent communicationintelligent signal processing |
| spellingShingle | Wenshi Xiao Zhongqiang Luo Qian Hu A Review of Research on Signal Modulation Recognition Based on Deep Learning Electronics automatic modulation recognition deep learning wireless communication intelligent communication intelligent signal processing |
| title | A Review of Research on Signal Modulation Recognition Based on Deep Learning |
| title_full | A Review of Research on Signal Modulation Recognition Based on Deep Learning |
| title_fullStr | A Review of Research on Signal Modulation Recognition Based on Deep Learning |
| title_full_unstemmed | A Review of Research on Signal Modulation Recognition Based on Deep Learning |
| title_short | A Review of Research on Signal Modulation Recognition Based on Deep Learning |
| title_sort | review of research on signal modulation recognition based on deep learning |
| topic | automatic modulation recognition deep learning wireless communication intelligent communication intelligent signal processing |
| url | https://www.mdpi.com/2079-9292/11/17/2764 |
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