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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Vydané v:	IEEE signal processing letters Ročník 24; číslo 11; s. 1626 - 1630
Hlavní autori:	Ali, Afan, Fan Yangyu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.11.2017
Predmet:	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
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