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Proceedings Paper • new

Deep learning based automatic signal modulation classification
Author(s): Jingyang Lu; Yi Li; Genshe Chen; Dan Shen; Xin Tian; Khanh Pham
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Paper Abstract

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to automatically classify signal modulation more efficiently, which can further help in radio frequency modeling and pattern recognition problem solving. Three different approaches Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) have been deployed and evaluated in the signal modulation classification. In this paper, the signals for training and validation are generated using our MATLAB based RF signal generator, which can simulate various types of modulated signal according to the configuration specification. The numerical results show that CNN network can outperform the DNN and RNN in terms of the signal modulation classification accuracy.

Paper Details

Date Published: 14 May 2019
PDF: 9 pages
Proc. SPIE 11017, Sensors and Systems for Space Applications XII, 110170M (14 May 2019); doi: 10.1117/12.2520544
Show Author Affiliations
Jingyang Lu, Intelligent Fusion Technology, Inc. (United States)
Yi Li, Intelligent Fusion Technology, Inc. (United States)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)
Dan Shen, Intelligent Fusion Technology, Inc. (United States)
Xin Tian, Intelligent Fusion Technology, Inc. (United States)
Khanh Pham, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 11017:
Sensors and Systems for Space Applications XII
Genshe Chen; Khanh D. Pham, Editor(s)

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