Automatic Modulation Classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints

We demonstrate a novel method for the automatic modulation classification based on a deep learning autoencoder network, trained by a nonnegativity constraint algorithm. The learning algorithm aims to constrain the negative weights, learns features that amount to a part-based representation of data,...

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Bibliographic Details

Published in:	IEEE signal processing letters Vol. 24; no. 11; pp. 1626 - 1630
Main Authors:	Ali, Afan, Fan Yangyu
Format:	Journal Article
Language:	English
Published:	IEEE 01.11.2017
Subjects:	Autoencoder automatic modulation classification Cost function cumulants deep learning networks Fading channels Feature extraction Machine learning Modulation nonnegativity constraints Signal processing algorithms Training
ISSN:	1070-9908, 1558-2361
Online Access:	Get full text
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