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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2021-10-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2020 |
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| Summary: | 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. |
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| ISSN: | 1007-2683 |