Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication
Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/7/1096 |
| _version_ | 1797589360681418752 |
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| author | Xiaohui Yao Honghui Yang Meiping Sheng |
| author_facet | Xiaohui Yao Honghui Yang Meiping Sheng |
| author_sort | Xiaohui Yao |
| collection | DOAJ |
| description | Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels. |
| first_indexed | 2024-03-11T01:05:32Z |
| format | Article |
| id | doaj.art-3fbb4acbfaea4e9fa2d3f6fa578c96dc |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T01:05:32Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-3fbb4acbfaea4e9fa2d3f6fa578c96dc2023-11-18T19:14:45ZengMDPI AGEntropy1099-43002023-07-01257109610.3390/e25071096Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater CommunicationXiaohui Yao0Honghui Yang1Meiping Sheng2School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaAutomatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.https://www.mdpi.com/1099-4300/25/7/1096automatic modulation classificationunderwater acoustic communication signalsgraph convolution networkfeature fusion |
| spellingShingle | Xiaohui Yao Honghui Yang Meiping Sheng Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication Entropy automatic modulation classification underwater acoustic communication signals graph convolution network feature fusion |
| title | Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication |
| title_full | Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication |
| title_fullStr | Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication |
| title_full_unstemmed | Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication |
| title_short | Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication |
| title_sort | feature fusion based on graph convolution network for modulation classification in underwater communication |
| topic | automatic modulation classification underwater acoustic communication signals graph convolution network feature fusion |
| url | https://www.mdpi.com/1099-4300/25/7/1096 |
| work_keys_str_mv | AT xiaohuiyao featurefusionbasedongraphconvolutionnetworkformodulationclassificationinunderwatercommunication AT honghuiyang featurefusionbasedongraphconvolutionnetworkformodulationclassificationinunderwatercommunication AT meipingsheng featurefusionbasedongraphconvolutionnetworkformodulationclassificationinunderwatercommunication |