Automatic Modulation Recognition Based on Deep Learning
Automatic modulation recognition (AMR) is critical to realize efficient spectrum sensing,spectrum management and spectrum utilization in non-cooperative communication scenarios.It is also an important prerequisite for efficient signal proces-sing.Traditional AMR methods based on pattern recognition...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2022-05-01
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| Series: | Jisuanji kexue |
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| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-5-266.pdf |
| _version_ | 1797845058192408576 |
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| author | JIAO Xiang, WEI Xiang-lin, XUE Yu, WANG Chao, DUAN Qiang |
| author_facet | JIAO Xiang, WEI Xiang-lin, XUE Yu, WANG Chao, DUAN Qiang |
| author_sort | JIAO Xiang, WEI Xiang-lin, XUE Yu, WANG Chao, DUAN Qiang |
| collection | DOAJ |
| description | Automatic modulation recognition (AMR) is critical to realize efficient spectrum sensing,spectrum management and spectrum utilization in non-cooperative communication scenarios.It is also an important prerequisite for efficient signal proces-sing.Traditional AMR methods based on pattern recognition need to extract features manually,which faces many problems such as high design complexity,low recognition accuracy and weak generalization ability.Therefore,practitioners turn to deep learning (DL) methods,which are good at extracting hidden features from the data,and propose a number of AMR-oriented deep neural network (ADNN) architectures.Compared with traditional methods,ADNN has achieved higher recognition accuracy,higher generalization ability and wider application range.This paper provides a comprehensive survey of ADNN to help practitioners understand the current research status in this field,and analyzes the future directions after pinpointing several open issues.Firstly,typical deep learning methods involved in ADNN design are introduced.Secondly,a few traditional AMR methods are briefly described.Thirdly,typical ADNNs are introduced in detail.Finally,a series of experiments are conducted on an open dataset to compare typical proposals,and several key research directions in this field are put forward. |
| first_indexed | 2024-04-09T17:32:21Z |
| format | Article |
| id | doaj.art-4cabb7098e884e4b9b6ae2c0d32211c1 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-04-09T17:32:21Z |
| publishDate | 2022-05-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-4cabb7098e884e4b9b6ae2c0d32211c12023-04-18T02:35:57ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2022-05-0149526627810.11896/jsjkx.211000085Automatic Modulation Recognition Based on Deep LearningJIAO Xiang, WEI Xiang-lin, XUE Yu, WANG Chao, DUAN Qiang01 School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China ;2 The 63rd Research Institute,National University of Defense Technology,Nanjing 210007,ChinaAutomatic modulation recognition (AMR) is critical to realize efficient spectrum sensing,spectrum management and spectrum utilization in non-cooperative communication scenarios.It is also an important prerequisite for efficient signal proces-sing.Traditional AMR methods based on pattern recognition need to extract features manually,which faces many problems such as high design complexity,low recognition accuracy and weak generalization ability.Therefore,practitioners turn to deep learning (DL) methods,which are good at extracting hidden features from the data,and propose a number of AMR-oriented deep neural network (ADNN) architectures.Compared with traditional methods,ADNN has achieved higher recognition accuracy,higher generalization ability and wider application range.This paper provides a comprehensive survey of ADNN to help practitioners understand the current research status in this field,and analyzes the future directions after pinpointing several open issues.Firstly,typical deep learning methods involved in ADNN design are introduced.Secondly,a few traditional AMR methods are briefly described.Thirdly,typical ADNNs are introduced in detail.Finally,a series of experiments are conducted on an open dataset to compare typical proposals,and several key research directions in this field are put forward.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-5-266.pdfdeep learning|deep neural network|automatic modulation recognition|security |
| spellingShingle | JIAO Xiang, WEI Xiang-lin, XUE Yu, WANG Chao, DUAN Qiang Automatic Modulation Recognition Based on Deep Learning Jisuanji kexue deep learning|deep neural network|automatic modulation recognition|security |
| title | Automatic Modulation Recognition Based on Deep Learning |
| title_full | Automatic Modulation Recognition Based on Deep Learning |
| title_fullStr | Automatic Modulation Recognition Based on Deep Learning |
| title_full_unstemmed | Automatic Modulation Recognition Based on Deep Learning |
| title_short | Automatic Modulation Recognition Based on Deep Learning |
| title_sort | automatic modulation recognition based on deep learning |
| topic | deep learning|deep neural network|automatic modulation recognition|security |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-5-266.pdf |
| work_keys_str_mv | AT jiaoxiangweixianglinxueyuwangchaoduanqiang automaticmodulationrecognitionbasedondeeplearning |