… Abstract—We demonstrate a novel method for the automatic modulation classification based
on a deep learning autoencoder network, trained by a nonnegativity constraint algorithm. …

… to identify the type of modulation. In this paper, a deep learning based modulation classification
method is developed for discriminating digital modulated signals. This proposed method …

… However, recently, deep neural networks, a branch of machine learning, have gained … Here,
we test the application of deep neural networks to the automatic modulation classification in …

… sparse autoencoder-based classifer, with unsorted input data features and called it unsorted
deep neural network … We apply k-sparse autoencoders [6] based DNN to classify high-order …

… , an automatic modulation recognition algorithm based on Stacked Sparse Autoencoder …
Abdi and Abstract:, ‘Survey of automatic modulation classification techniques: classical …

… proposes a modulation classification method based on Stacked Denoising Sparse Autoencoder
(… The aim of this paper is to achieve digital modulation classification for CR system. A …

… stacked denoising autoencoder, then we use the reconstructed signals as a set of input to
train a deep neural network which is initialized by the stacked denoising autoencoder we have …

… a digital modulation classification in application of CR. A completed classification scheme
has … Automatic modulation classification using combination of genetic program and knn. IEEE …

… we use the stacked denoising sparse autoencoder proposed in [22] as the base structure to
state that the advantage of CNN. Besides, paper [20] show that radio modulation recognition …

… of deep learning in the area of automatic modulation … feature learning based neural network
called sparse-autoencoder … network to deal with the problem of modulation classification. …