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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Vydáno v:IEEE signal processing letters Ročník 24; číslo 11; s. 1626 - 1630
Hlavní autoři: Ali, Afan, Fan Yangyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.11.2017
Témata:
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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Shrnutí: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