A survey of deep learning applied to radio signal modulation recognition
Modulation recognition of radio signals plays a vital role in radio monitoring and spectrum management. As the deep learning network in artificial neural network has the powerful ability of representation learning which can automatically extract various complex features from the original data, explo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
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National Computer System Engineering Research Institute of China
2019-05-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000101121 |
| _version_ | 1818311546604879872 |
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| author | Yuan Bingqing Wang Yansong Zheng Liugang |
| author_facet | Yuan Bingqing Wang Yansong Zheng Liugang |
| author_sort | Yuan Bingqing |
| collection | DOAJ |
| description | Modulation recognition of radio signals plays a vital role in radio monitoring and spectrum management. As the deep learning network in artificial neural network has the powerful ability of representation learning which can automatically extract various complex features from the original data, exploring the modulation identification of radio signals based on deep learning is one of the main development trends in the field of radio monitoring. This paper introduces some application results and existing problems of deep learning in radio signal modulation recognition. Combined with the actual needs of the work, this review puts forward some ideas for deep learning in the modulation recognition of radio signals, such as further improving the recognition range and the recognition accuracy, especially at low SNR; seeking some new deep learning hybrid architecture for radio signal modulation recognition. |
| first_indexed | 2024-12-13T08:03:41Z |
| format | Article |
| id | doaj.art-c6f88d9ce48a4b70af9a4bbc0a994bab |
| institution | Directory Open Access Journal |
| issn | 0258-7998 |
| language | zho |
| last_indexed | 2024-12-13T08:03:41Z |
| publishDate | 2019-05-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj.art-c6f88d9ce48a4b70af9a4bbc0a994bab2022-12-21T23:54:21ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982019-05-014551410.16157/j.issn.0258-7998.1833003000101121A survey of deep learning applied to radio signal modulation recognitionYuan Bingqing0Wang Yansong1Zheng Liugang2Shanghai Station of State Radio Monitoring Centre，Shanghai 201419，ChinaShanghai Station of State Radio Monitoring Centre，Shanghai 201419，ChinaShanghai Station of State Radio Monitoring Centre，Shanghai 201419，ChinaModulation recognition of radio signals plays a vital role in radio monitoring and spectrum management. As the deep learning network in artificial neural network has the powerful ability of representation learning which can automatically extract various complex features from the original data, exploring the modulation identification of radio signals based on deep learning is one of the main development trends in the field of radio monitoring. This paper introduces some application results and existing problems of deep learning in radio signal modulation recognition. Combined with the actual needs of the work, this review puts forward some ideas for deep learning in the modulation recognition of radio signals, such as further improving the recognition range and the recognition accuracy, especially at low SNR; seeking some new deep learning hybrid architecture for radio signal modulation recognition.http://www.chinaaet.com/article/3000101121modulation recognitiondeep learningconvolution neural networkrecurrent neural network |
| spellingShingle | Yuan Bingqing Wang Yansong Zheng Liugang A survey of deep learning applied to radio signal modulation recognition Dianzi Jishu Yingyong modulation recognition deep learning convolution neural network recurrent neural network |
| title | A survey of deep learning applied to radio signal modulation recognition |
| title_full | A survey of deep learning applied to radio signal modulation recognition |
| title_fullStr | A survey of deep learning applied to radio signal modulation recognition |
| title_full_unstemmed | A survey of deep learning applied to radio signal modulation recognition |
| title_short | A survey of deep learning applied to radio signal modulation recognition |
| title_sort | survey of deep learning applied to radio signal modulation recognition |
| topic | modulation recognition deep learning convolution neural network recurrent neural network |
| url | http://www.chinaaet.com/article/3000101121 |
| work_keys_str_mv | AT yuanbingqing asurveyofdeeplearningappliedtoradiosignalmodulationrecognition AT wangyansong asurveyofdeeplearningappliedtoradiosignalmodulationrecognition AT zhengliugang asurveyofdeeplearningappliedtoradiosignalmodulationrecognition AT yuanbingqing surveyofdeeplearningappliedtoradiosignalmodulationrecognition AT wangyansong surveyofdeeplearningappliedtoradiosignalmodulationrecognition AT zhengliugang surveyofdeeplearningappliedtoradiosignalmodulationrecognition |