A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network
Deep-learning is widely used in modulation classification to reduce labor and improve the efficiency. Graph convolutional network (GCN) is a type of feature extraction network for graph data. Considering the signals as graph nodes and the similarity of each signal as an edge, the GCN propagates node...
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| Format: | Article |
| Language: | English |
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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/10493016/ |
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| author | Huali Zhu Hua Xu Yunhao Shi Yue Zhang Lei Jiang |
| author_facet | Huali Zhu Hua Xu Yunhao Shi Yue Zhang Lei Jiang |
| author_sort | Huali Zhu |
| collection | DOAJ |
| description | Deep-learning is widely used in modulation classification to reduce labor and improve the efficiency. Graph convolutional network (GCN) is a type of feature extraction network for graph data. Considering the signals as graph nodes and the similarity of each signal as an edge, the GCN propagates node information to similar nodes along the edges. GCN extracts more features and achieves better classification results, particularly for characterless examples. In this paper, we propose a modulation classification algorithm based on a feature-embedding GCN (FE-GCN). It comprises three parts: feature-embedding network (FEN), similarity adjacent matrix calculation network (SAMCN), and graph convolutional classification network (GCCN). The FEN embeds the signal data into a one-dimensional feature vector. The SAMCN calculates the similarity of all signal feature vectors to a matrix using a single convolutional neural network (CNN). The GCCN is used to extract the final features and classify the signals in a graph. Simulation results on the public dataset RML2016.10A show that the FE-GCN performs effectively and outperforms a series of advanced deep-learning methods. |
| format | Article |
| id | doaj-art-a97a28d1fc9248b2bc4b424d2c4333fc |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a97a28d1fc9248b2bc4b424d2c4333fc2025-07-24T23:02:33ZengIEEEIEEE Access2169-35362025-01-011312505712506510.1109/ACCESS.2024.338566310493016A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional NetworkHuali Zhu0https://orcid.org/0000-0002-9906-7445Hua Xu1Yunhao Shi2https://orcid.org/0000-0003-3493-5448Yue Zhang3https://orcid.org/0000-0002-6664-8761Lei Jiang4Information and Navigation College, Air Force Engineering University, Xi’an, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an, ChinaInformation and Navigation College, Air Force Engineering University, Xi’an, ChinaDeep-learning is widely used in modulation classification to reduce labor and improve the efficiency. Graph convolutional network (GCN) is a type of feature extraction network for graph data. Considering the signals as graph nodes and the similarity of each signal as an edge, the GCN propagates node information to similar nodes along the edges. GCN extracts more features and achieves better classification results, particularly for characterless examples. In this paper, we propose a modulation classification algorithm based on a feature-embedding GCN (FE-GCN). It comprises three parts: feature-embedding network (FEN), similarity adjacent matrix calculation network (SAMCN), and graph convolutional classification network (GCCN). The FEN embeds the signal data into a one-dimensional feature vector. The SAMCN calculates the similarity of all signal feature vectors to a matrix using a single convolutional neural network (CNN). The GCCN is used to extract the final features and classify the signals in a graph. Simulation results on the public dataset RML2016.10A show that the FE-GCN performs effectively and outperforms a series of advanced deep-learning methods.https://ieeexplore.ieee.org/document/10493016/Feature embeddingmodulation recognitionGCN |
| spellingShingle | Huali Zhu Hua Xu Yunhao Shi Yue Zhang Lei Jiang A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network IEEE Access Feature embedding modulation recognition GCN |
| title | A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network |
| title_full | A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network |
| title_fullStr | A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network |
| title_full_unstemmed | A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network |
| title_short | A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network |
| title_sort | modulation classification algorithm based on feature embedding graph convolutional network |
| topic | Feature embedding modulation recognition GCN |
| url | https://ieeexplore.ieee.org/document/10493016/ |
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