Mixed signal modulation recognition method based on temporal depth residual shrinkage network
Deep learning-based automatic signal modulation recognition has generally outperformed traditional methods in terms of classification accuracy and transferability, garnering widespread attention. However, most existing methods are designed to recognize single signal samples and are not applicable to...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-10-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024207/ |
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| _version_ | 1841528859599568896 |
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| author | LIU Jinghua WEI Xianglin FAN Jianhua HU Yongyang WANG Xiaobo YU Bing |
| author_facet | LIU Jinghua WEI Xianglin FAN Jianhua HU Yongyang WANG Xiaobo YU Bing |
| author_sort | LIU Jinghua |
| collection | DOAJ |
| description | Deep learning-based automatic signal modulation recognition has generally outperformed traditional methods in terms of classification accuracy and transferability, garnering widespread attention. However, most existing methods are designed to recognize single signal samples and are not applicable to recognize scenarios involving overlapping signals. To address this limitation, a modulation recognition method for aliased signals was investigated and a temporal deep residual shrinkage network model by integrating LSTM and DRSN was developed. There were three key modules in the model: a residual module, a shrinkage module, and a LSTM module. Salient information from overlapping signals was extracted by the residual module and the shrinkage module and decision thresholds were adaptively generated, while the LSTM module is tasked with extracting temporal hidden signals within the aliased data. The recognition accuracy of aliased signals was enhanced by the combination of these modules significantly. Testing on both public and private datasets demonstrates that the proposed method outperforms five state-of-the-art approaches, achieving an average recognition and classification accuracy of 92.7% under high signal-to-noise ratio conditions. Notably, the recognition accuracy for 12 out of 21 types of aliased signals approaches 100%. |
| format | Article |
| id | doaj-art-728ab41adc5749d7bc05a6c65a93c3b0 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-728ab41adc5749d7bc05a6c65a93c3b02025-01-15T03:34:06ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-10-0140273876739102Mixed signal modulation recognition method based on temporal depth residual shrinkage networkLIU JinghuaWEI XianglinFAN JianhuaHU YongyangWANG XiaoboYU BingDeep learning-based automatic signal modulation recognition has generally outperformed traditional methods in terms of classification accuracy and transferability, garnering widespread attention. However, most existing methods are designed to recognize single signal samples and are not applicable to recognize scenarios involving overlapping signals. To address this limitation, a modulation recognition method for aliased signals was investigated and a temporal deep residual shrinkage network model by integrating LSTM and DRSN was developed. There were three key modules in the model: a residual module, a shrinkage module, and a LSTM module. Salient information from overlapping signals was extracted by the residual module and the shrinkage module and decision thresholds were adaptively generated, while the LSTM module is tasked with extracting temporal hidden signals within the aliased data. The recognition accuracy of aliased signals was enhanced by the combination of these modules significantly. Testing on both public and private datasets demonstrates that the proposed method outperforms five state-of-the-art approaches, achieving an average recognition and classification accuracy of 92.7% under high signal-to-noise ratio conditions. Notably, the recognition accuracy for 12 out of 21 types of aliased signals approaches 100%.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024207/modulation recognitionmixed signaldeep residual fast shrinking networkdeep learning |
| spellingShingle | LIU Jinghua WEI Xianglin FAN Jianhua HU Yongyang WANG Xiaobo YU Bing Mixed signal modulation recognition method based on temporal depth residual shrinkage network Dianxin kexue modulation recognition mixed signal deep residual fast shrinking network deep learning |
| title | Mixed signal modulation recognition method based on temporal depth residual shrinkage network |
| title_full | Mixed signal modulation recognition method based on temporal depth residual shrinkage network |
| title_fullStr | Mixed signal modulation recognition method based on temporal depth residual shrinkage network |
| title_full_unstemmed | Mixed signal modulation recognition method based on temporal depth residual shrinkage network |
| title_short | Mixed signal modulation recognition method based on temporal depth residual shrinkage network |
| title_sort | mixed signal modulation recognition method based on temporal depth residual shrinkage network |
| topic | modulation recognition mixed signal deep residual fast shrinking network deep learning |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024207/ |
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