Modulation Recognition of MPSK Signals Based on Novel Convolutional Neural Network
With the rapid development of 釭tificial intelligence, convolutional neural network is more and more applied to the field of communication signal modulation recognition. Aiming at the problem of low recognition accuracy of digital signals at low SNR, a modulation recognition model named lnceptionresn...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2021-10-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2020 |
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| author | WANG Ai-li ZHANG Jia-wei JIANG Kai-yuan WU Hai一bin Yuji Iwahori |
| author_facet | WANG Ai-li ZHANG Jia-wei JIANG Kai-yuan WU Hai一bin Yuji Iwahori |
| author_sort | WANG Ai-li |
| collection | DOAJ |
| description | With the rapid development of 釭tificial intelligence, convolutional neural network is more and more applied to the field of communication signal modulation recognition. Aiming at the problem of low recognition accuracy of digital signals at low SNR, a modulation recognition model named lnceptionresnetV2-TA was studied by combining InceptionresnetV2 network with migration adaptation to identify the modulation mode of MPSK signals. The results show that when the SNR is 3d8, the recognition rate of InceptionresnetV2-TA for BPSK is
99. 33%, which is 3% higher than that of the suboptimal model lnceptionresnetV2. The recognition rate of QPSK is 95. 33% , which is 2% higher than lnceptionresnetV2. The recognition rate of 8PSK is 86. 33% , which is 5% higher than that of Inceptionresnetv2. The above results indicate that InceptionresnetV2-TA combined with migration adaptation has higher identification accuracy of BPSK, QPSK and 8PSK at low SNR than other comparison methods. At the same time, the validity of the modulation recognition model is verified. |
| format | Article |
| id | doaj-art-4ae52b24ef304de7a66ea14fdd729a21 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2021-10-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-4ae52b24ef304de7a66ea14fdd729a212025-08-20T03:57:40ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832021-10-0126059710310.15938/j.jhust.2021.05.013Modulation Recognition of MPSK Signals Based on Novel Convolutional Neural Network WANG Ai-li0ZHANG Jia-wei1JIANG Kai-yuan2WU Hai一bin3Yuji Iwahori4School of Measurement-Conlrol Technology and Communication Engineering, Harbin Unive1-sity of Science and Technology, Harbin 150080, ChinaSchool of Measurement-Conlrol Technology and Communication Engineering, Harbin Unive1-sity of Science and Technology, Harbin 150080, ChinaSchool of Measurement-Conlrol Technology and Communication Engineering, Harbin Unive1-sity of Science and Technology, Harbin 150080, ChinaSchool of Measurement-Conlrol Technology and Communication Engineering, Harbin Unive1-sity of Science and Technology, Harbin 150080, ChinaDepartment of Computer Science, Chubu University, Aichi 487-8501, JapanWith the rapid development of 釭tificial intelligence, convolutional neural network is more and more applied to the field of communication signal modulation recognition. Aiming at the problem of low recognition accuracy of digital signals at low SNR, a modulation recognition model named lnceptionresnetV2-TA was studied by combining InceptionresnetV2 network with migration adaptation to identify the modulation mode of MPSK signals. The results show that when the SNR is 3d8, the recognition rate of InceptionresnetV2-TA for BPSK is 99. 33%, which is 3% higher than that of the suboptimal model lnceptionresnetV2. The recognition rate of QPSK is 95. 33% , which is 2% higher than lnceptionresnetV2. The recognition rate of 8PSK is 86. 33% , which is 5% higher than that of Inceptionresnetv2. The above results indicate that InceptionresnetV2-TA combined with migration adaptation has higher identification accuracy of BPSK, QPSK and 8PSK at low SNR than other comparison methods. At the same time, the validity of the modulation recognition model is verified. https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2020modulation recognitionconvolutional neural nehvorktransfer adaptationlnceptionresnetv2 |
| spellingShingle | WANG Ai-li ZHANG Jia-wei JIANG Kai-yuan WU Hai一bin Yuji Iwahori Modulation Recognition of MPSK Signals Based on Novel Convolutional Neural Network Journal of Harbin University of Science and Technology modulation recognition convolutional neural nehvork transfer adaptation lnceptionresnetv2 |
| title | Modulation Recognition of MPSK Signals Based on Novel Convolutional Neural Network |
| title_full | Modulation Recognition of MPSK Signals Based on Novel Convolutional Neural Network |
| title_fullStr | Modulation Recognition of MPSK Signals Based on Novel Convolutional Neural Network |
| title_full_unstemmed | Modulation Recognition of MPSK Signals Based on Novel Convolutional Neural Network |
| title_short | Modulation Recognition of MPSK Signals Based on Novel Convolutional Neural Network |
| title_sort | modulation recognition of mpsk signals based on novel convolutional neural network |
| topic | modulation recognition convolutional neural nehvork transfer adaptation lnceptionresnetv2 |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2020 |
| work_keys_str_mv | AT wangaili modulationrecognitionofmpsksignalsbasedonnovelconvolutionalneuralnetwork AT zhangjiawei modulationrecognitionofmpsksignalsbasedonnovelconvolutionalneuralnetwork AT jiangkaiyuan modulationrecognitionofmpsksignalsbasedonnovelconvolutionalneuralnetwork AT wuhaiyībin modulationrecognitionofmpsksignalsbasedonnovelconvolutionalneuralnetwork AT yujiiwahori modulationrecognitionofmpsksignalsbasedonnovelconvolutionalneuralnetwork |