A hybrid model for automatic modulation classification based on residual neural networks and long short term memory
This paper introduces a deep learning (DL)-based Automatic Modulation Classification (AMC) model. Our model is considered to be a receiver with a modulation classifier that is capable of differentiating ten modulation techniques. The classifier combines the residual neural network (ResNet) and the l...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-03-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016822005488 |
| _version_ | 1797884503108091904 |
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| author | Mohamed M. Elsagheer Safwat M. Ramzy |
| author_facet | Mohamed M. Elsagheer Safwat M. Ramzy |
| author_sort | Mohamed M. Elsagheer |
| collection | DOAJ |
| description | This paper introduces a deep learning (DL)-based Automatic Modulation Classification (AMC) model. Our model is considered to be a receiver with a modulation classifier that is capable of differentiating ten modulation techniques. The classifier combines the residual neural network (ResNet) and the long short-term memory network (LSTM). The ResNet boosts the accuracy in deep neural networks, and LSTM improves the classifier’s performance by passing the time-series previous state information to the current state. This paper demonstrates that the proposed model achieves 92% peak recognition accuracy at 18 dB SNR. It is higher than the ResNet by 11.4%, the CNN network by 4.7%, and the CLDNN network by 2%. Moreover, it delivers more than 90% classification accuracy at SNR above 0 dB. Additionally, it improves the classification accuracy at low SNR by achieving 85.5% accuracy at −2 dB SNR. Furthermore, it advances the recognition accuracy of various modulation recognition methods by more than 98% at SNR above 0 dB. |
| first_indexed | 2024-04-10T04:08:41Z |
| format | Article |
| id | doaj.art-bdb105ca273c4ebfb519b0edc7e63b71 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-10T04:08:41Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-bdb105ca273c4ebfb519b0edc7e63b712023-03-13T04:15:10ZengElsevierAlexandria Engineering Journal1110-01682023-03-0167117128A hybrid model for automatic modulation classification based on residual neural networks and long short term memoryMohamed M. Elsagheer0Safwat M. Ramzy1Department of Electrical Engineering, Faculty of Engineering. Sohag University, Sohag, EgyptDepartment of Electrical Engineering, Faculty of Engineering. Sohag University, Sohag, EgyptThis paper introduces a deep learning (DL)-based Automatic Modulation Classification (AMC) model. Our model is considered to be a receiver with a modulation classifier that is capable of differentiating ten modulation techniques. The classifier combines the residual neural network (ResNet) and the long short-term memory network (LSTM). The ResNet boosts the accuracy in deep neural networks, and LSTM improves the classifier’s performance by passing the time-series previous state information to the current state. This paper demonstrates that the proposed model achieves 92% peak recognition accuracy at 18 dB SNR. It is higher than the ResNet by 11.4%, the CNN network by 4.7%, and the CLDNN network by 2%. Moreover, it delivers more than 90% classification accuracy at SNR above 0 dB. Additionally, it improves the classification accuracy at low SNR by achieving 85.5% accuracy at −2 dB SNR. Furthermore, it advances the recognition accuracy of various modulation recognition methods by more than 98% at SNR above 0 dB.http://www.sciencedirect.com/science/article/pii/S1110016822005488Deep learningAMCResidual neural networkLSTMSNR |
| spellingShingle | Mohamed M. Elsagheer Safwat M. Ramzy A hybrid model for automatic modulation classification based on residual neural networks and long short term memory Alexandria Engineering Journal Deep learning AMC Residual neural network LSTM SNR |
| title | A hybrid model for automatic modulation classification based on residual neural networks and long short term memory |
| title_full | A hybrid model for automatic modulation classification based on residual neural networks and long short term memory |
| title_fullStr | A hybrid model for automatic modulation classification based on residual neural networks and long short term memory |
| title_full_unstemmed | A hybrid model for automatic modulation classification based on residual neural networks and long short term memory |
| title_short | A hybrid model for automatic modulation classification based on residual neural networks and long short term memory |
| title_sort | hybrid model for automatic modulation classification based on residual neural networks and long short term memory |
| topic | Deep learning AMC Residual neural network LSTM SNR |
| url | http://www.sciencedirect.com/science/article/pii/S1110016822005488 |
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