A Novel and Effective Model for Automatic Modulation Classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-Cross
Automatic Modulation Classification (AMC) is a pivotal technology in various communication systems. In recent years, deep learning (DL) has been widely applied in AMC methods due to its powerful feature extraction capabilities. However, currently proposed AMC methods still have room for improvement...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10786990/ |
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| author | Jianzheng Lin |
| author_facet | Jianzheng Lin |
| author_sort | Jianzheng Lin |
| collection | DOAJ |
| description | Automatic Modulation Classification (AMC) is a pivotal technology in various communication systems. In recent years, deep learning (DL) has been widely applied in AMC methods due to its powerful feature extraction capabilities. However, currently proposed AMC methods still have room for improvement in classification performance. To further enhance the prediction accuracy of AMC, we propose a new model called MMH-AMC: Automatic Modulation Classification with Multi-BIGRU, Multi-Encoder, and Hyber-Cross. Multi-BIGRU employs a multi-layer bidirectional GRU architecture to extract deep distribution patterns of AP data from both directions simultaneously. Multi-Encoder adopts a multi-layer encoder architecture with a core of Multi-Head Attention, aiming to extract deep-level IQ data distributions. To unify the feature distributions outputted by Multi-Encoder and Hyber-Cross modules, we designed and utilized the Hyber-Cross module. To validate the model’s performance, we compared six different deep learning models and achieved the best performance in various scenarios. |
| format | Article |
| id | doaj-art-d1e38da17cce441991b6fb02e7542cb0 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d1e38da17cce441991b6fb02e7542cb02025-01-31T23:04:30ZengIEEEIEEE Access2169-35362025-01-0113202602027710.1109/ACCESS.2024.351407810786990A Novel and Effective Model for Automatic Modulation Classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-CrossJianzheng Lin0College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, ChinaAutomatic Modulation Classification (AMC) is a pivotal technology in various communication systems. In recent years, deep learning (DL) has been widely applied in AMC methods due to its powerful feature extraction capabilities. However, currently proposed AMC methods still have room for improvement in classification performance. To further enhance the prediction accuracy of AMC, we propose a new model called MMH-AMC: Automatic Modulation Classification with Multi-BIGRU, Multi-Encoder, and Hyber-Cross. Multi-BIGRU employs a multi-layer bidirectional GRU architecture to extract deep distribution patterns of AP data from both directions simultaneously. Multi-Encoder adopts a multi-layer encoder architecture with a core of Multi-Head Attention, aiming to extract deep-level IQ data distributions. To unify the feature distributions outputted by Multi-Encoder and Hyber-Cross modules, we designed and utilized the Hyber-Cross module. To validate the model’s performance, we compared six different deep learning models and achieved the best performance in various scenarios.https://ieeexplore.ieee.org/document/10786990/Automatic modulation classificationmulti-BIGRUmulti-encoderhyber-cross |
| spellingShingle | Jianzheng Lin A Novel and Effective Model for Automatic Modulation Classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-Cross IEEE Access Automatic modulation classification multi-BIGRU multi-encoder hyber-cross |
| title | A Novel and Effective Model for Automatic Modulation Classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-Cross |
| title_full | A Novel and Effective Model for Automatic Modulation Classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-Cross |
| title_fullStr | A Novel and Effective Model for Automatic Modulation Classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-Cross |
| title_full_unstemmed | A Novel and Effective Model for Automatic Modulation Classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-Cross |
| title_short | A Novel and Effective Model for Automatic Modulation Classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-Cross |
| title_sort | novel and effective model for automatic modulation classification prediction based on multi bigru multi encoder and hyper cross |
| topic | Automatic modulation classification multi-BIGRU multi-encoder hyber-cross |
| url | https://ieeexplore.ieee.org/document/10786990/ |
| work_keys_str_mv | AT jianzhenglin anovelandeffectivemodelforautomaticmodulationclassificationpredictionbasedonmultibigrumultiencoderandhypercross AT jianzhenglin novelandeffectivemodelforautomaticmodulationclassificationpredictionbasedonmultibigrumultiencoderandhypercross |