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A robust modulation classification method using convolutional neural networks
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Zhou, Siyang, Yin, Zhendong, Wu, Zhilu, Chen, Yunfei, Zhao, Nan and Yang, Zhutian (2019) A robust modulation classification method using convolutional neural networks. Eurasip Journal on Advances in Signal Processing (21). doi:10.1186/s13634-019-0616-6 ISSN 1687-6172.
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WRAP-robust-modulation-classification-method-using-convolutional-neural-networks-Chen-2019.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (334Kb) |
Official URL: https://doi.org/10.1186/s13634-019-0616-6
Abstract
Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Modulation (Electronics) | |||||||||||||||
Journal or Publication Title: | Eurasip Journal on Advances in Signal Processing | |||||||||||||||
Publisher: | SpringerOpen | |||||||||||||||
ISSN: | 1687-6172 | |||||||||||||||
Official Date: | 29 March 2019 | |||||||||||||||
Dates: |
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Number: | 21 | |||||||||||||||
DOI: | 10.1186/s13634-019-0616-6 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 27 March 2019 | |||||||||||||||
Date of first compliant Open Access: | 2 May 2019 | |||||||||||||||
RIOXX Funder/Project Grant: |
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