Automatic Modulation Classification in Time-Varying Channels Based on Deep Learning
Automatic modulation classification (AMC) is an important technology in military signal reconnaissance and civilian communications such as cognitive radios. Most of the existing works focused on the AMC in additional white Gaussian noise channels, but the AMC in time-varying wireless channels is mor...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9245499/ |
| _version_ | 1818330456475566080 |
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| author | Yu Zhou Tian Lin Yu Zhu |
| author_facet | Yu Zhou Tian Lin Yu Zhu |
| author_sort | Yu Zhou |
| collection | DOAJ |
| description | Automatic modulation classification (AMC) is an important technology in military signal reconnaissance and civilian communications such as cognitive radios. Most of the existing works focused on the AMC in additional white Gaussian noise channels, but the AMC in time-varying wireless channels is more practical and challenging. In this article, we investigate the AMC in time-varying channels by using the deep learning method for high classification accuracy. Specifically, we take the modulation constellation diagram (CD) as the key feature and propose a slotted constellation diagram (slotted-CD) scheme in order to extract the feature of the time-evolution of the CD due to channel variation. We then develop an advanced neural network for modulation classification, where the output sub-images from the slotted-CD feature extractor are first processed separately by a number of parallel convolutional neural networks and then further processed by a recurrent neural network for exploring their time relationship. Experimental results show that the proposed AMC scheme achieves higher classification accuracy in both slow and fast fading channels when compared with the traditional deep learning based AMC schemes. Such performance improvement can be clearly illustrated by visualizing the outputs of the convolutional layers of the classifier. We also show that visualization can help optimize the parameters of the AMC neural networks. |
| first_indexed | 2024-12-13T13:04:15Z |
| format | Article |
| id | doaj.art-3bce36f8bdbd4884b3238ec2b25c439b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:04:15Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3bce36f8bdbd4884b3238ec2b25c439b2022-12-21T23:44:52ZengIEEEIEEE Access2169-35362020-01-01819750819752210.1109/ACCESS.2020.30349429245499Automatic Modulation Classification in Time-Varying Channels Based on Deep LearningYu Zhou0https://orcid.org/0000-0003-3144-5726Tian Lin1https://orcid.org/0000-0001-6160-579XYu Zhu2https://orcid.org/0000-0003-2303-5567Department of Communication Science and Engineering, Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, ChinaDepartment of Communication Science and Engineering, Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, ChinaDepartment of Communication Science and Engineering, Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, ChinaAutomatic modulation classification (AMC) is an important technology in military signal reconnaissance and civilian communications such as cognitive radios. Most of the existing works focused on the AMC in additional white Gaussian noise channels, but the AMC in time-varying wireless channels is more practical and challenging. In this article, we investigate the AMC in time-varying channels by using the deep learning method for high classification accuracy. Specifically, we take the modulation constellation diagram (CD) as the key feature and propose a slotted constellation diagram (slotted-CD) scheme in order to extract the feature of the time-evolution of the CD due to channel variation. We then develop an advanced neural network for modulation classification, where the output sub-images from the slotted-CD feature extractor are first processed separately by a number of parallel convolutional neural networks and then further processed by a recurrent neural network for exploring their time relationship. Experimental results show that the proposed AMC scheme achieves higher classification accuracy in both slow and fast fading channels when compared with the traditional deep learning based AMC schemes. Such performance improvement can be clearly illustrated by visualizing the outputs of the convolutional layers of the classifier. We also show that visualization can help optimize the parameters of the AMC neural networks.https://ieeexplore.ieee.org/document/9245499/Automatic modulation classificationconstellation diagramtime-varyingconvolutional neural networkbidirectional long short-term memory network |
| spellingShingle | Yu Zhou Tian Lin Yu Zhu Automatic Modulation Classification in Time-Varying Channels Based on Deep Learning IEEE Access Automatic modulation classification constellation diagram time-varying convolutional neural network bidirectional long short-term memory network |
| title | Automatic Modulation Classification in Time-Varying Channels Based on Deep Learning |
| title_full | Automatic Modulation Classification in Time-Varying Channels Based on Deep Learning |
| title_fullStr | Automatic Modulation Classification in Time-Varying Channels Based on Deep Learning |
| title_full_unstemmed | Automatic Modulation Classification in Time-Varying Channels Based on Deep Learning |
| title_short | Automatic Modulation Classification in Time-Varying Channels Based on Deep Learning |
| title_sort | automatic modulation classification in time varying channels based on deep learning |
| topic | Automatic modulation classification constellation diagram time-varying convolutional neural network bidirectional long short-term memory network |
| url | https://ieeexplore.ieee.org/document/9245499/ |
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