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,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE signal processing letters Jg. 24; H. 11; S. 1626 - 1630
Hauptverfasser: Ali, Afan, Fan Yangyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2017
Schlagworte:
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-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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, and disentangles a more meaningful hidden structure. The performance of this algorithm is tested on the fourth-order cumulants of the modulated signals. The results indicate that the autoencoder with nonnegativity constraint (ANC) improves the sparsity and minimizes the reconstruction error in comparison with the conventional sparse autoencoder. The classification accuracy of an ANC based deep network shows improved accuracy under limited signal length and fading channel.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2017.2752459