Modulation recognition method based on multiscale convolutional fusion coding networks
To address the issue of insufficient feature extraction in existing modulation recognition methods that limited classification accuracy, a Transformer-based modulation recognition method was proposed. Convolutional kernels of varying sizes were employed to enhance multi-scale signal feature extracti...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2025-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025137/ |
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| _version_ | 1849227164862382080 |
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| author | LI Guojun ZHU Siyuan ZHENG Jianzhong WANG Jie YE Changrong |
| author_facet | LI Guojun ZHU Siyuan ZHENG Jianzhong WANG Jie YE Changrong |
| author_sort | LI Guojun |
| collection | DOAJ |
| description | To address the issue of insufficient feature extraction in existing modulation recognition methods that limited classification accuracy, a Transformer-based modulation recognition method was proposed. Convolutional kernels of varying sizes were employed to enhance multi-scale signal feature extraction, followed by feature fusion to strengthen learning capability while reducing computational demands. A multi-head self-attention mechanism was utilized to enable parallel processing for capturing diverse signal characteristics. A dual-branch multilayer perceptron structure was introduced to further improve adaptability and diversity learning while accelerating operational speed. Experimental results demonstrated the model's robust stability and generalization capability, showing minimal performance variation under different test batch sizes with fixed training batches. On the RML2018.01A dataset, the proposed model achieve over 96% classification accuracy at 10 dB. |
| format | Article |
| id | doaj-art-842e92169f324f8f945aedd8f77681e5 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-842e92169f324f8f945aedd8f77681e52025-08-23T19:00:08ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-01-01112123248161Modulation recognition method based on multiscale convolutional fusion coding networksLI GuojunZHU SiyuanZHENG JianzhongWANG JieYE ChangrongTo address the issue of insufficient feature extraction in existing modulation recognition methods that limited classification accuracy, a Transformer-based modulation recognition method was proposed. Convolutional kernels of varying sizes were employed to enhance multi-scale signal feature extraction, followed by feature fusion to strengthen learning capability while reducing computational demands. A multi-head self-attention mechanism was utilized to enable parallel processing for capturing diverse signal characteristics. A dual-branch multilayer perceptron structure was introduced to further improve adaptability and diversity learning while accelerating operational speed. Experimental results demonstrated the model's robust stability and generalization capability, showing minimal performance variation under different test batch sizes with fixed training batches. On the RML2018.01A dataset, the proposed model achieve over 96% classification accuracy at 10 dB.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025137/convolutional neural networkmodulation classificationTransformermulti-scale fusionmultilayer perceptron |
| spellingShingle | LI Guojun ZHU Siyuan ZHENG Jianzhong WANG Jie YE Changrong Modulation recognition method based on multiscale convolutional fusion coding networks Tongxin xuebao convolutional neural network modulation classification Transformer multi-scale fusion multilayer perceptron |
| title | Modulation recognition method based on multiscale convolutional fusion coding networks |
| title_full | Modulation recognition method based on multiscale convolutional fusion coding networks |
| title_fullStr | Modulation recognition method based on multiscale convolutional fusion coding networks |
| title_full_unstemmed | Modulation recognition method based on multiscale convolutional fusion coding networks |
| title_short | Modulation recognition method based on multiscale convolutional fusion coding networks |
| title_sort | modulation recognition method based on multiscale convolutional fusion coding networks |
| topic | convolutional neural network modulation classification Transformer multi-scale fusion multilayer perceptron |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025137/ |
| work_keys_str_mv | AT liguojun modulationrecognitionmethodbasedonmultiscaleconvolutionalfusioncodingnetworks AT zhusiyuan modulationrecognitionmethodbasedonmultiscaleconvolutionalfusioncodingnetworks AT zhengjianzhong modulationrecognitionmethodbasedonmultiscaleconvolutionalfusioncodingnetworks AT wangjie modulationrecognitionmethodbasedonmultiscaleconvolutionalfusioncodingnetworks AT yechangrong modulationrecognitionmethodbasedonmultiscaleconvolutionalfusioncodingnetworks |