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Automatic Modulation Classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints
Ist Teil von
IEEE signal processing letters, 2017-11, Vol.24 (11), p.1626-1630
Ort / Verlag
IEEE
Erscheinungsjahr
2017
Quelle
IEL
Beschreibungen/Notizen
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.