… Automatic modulation classification enabled by deep learning approach has been proposed
in this paper. As a parallel computing model, a deep learning based classifier named CNN-…

… use of the DL in modulation classification, which is a major … the task complexity in modulation
classification. In this paper, we … methods to represent modulated signals in data formats with …

… neural network architectures suitable for the modulation classification task, and suggesting …
, we study different deep neural network architectures for the task of modulation classification, …

… Our modulation classification task is to decide which modulation scheme has been utilized
with the knowledge of the N sample received vector y = [y(1),y(2), ··· .y(N)]T . This paper …

… control (MAC) protocol classification [11]–[13], … modulation classification and, specifically,
focuses on the signal representation and data preprocessing aspect in modulation classification…

… Automatic modulation classification (AMC) is a novel and … In recent years, deep learning (DL)
has been considered one of the … , “Deep neural network for robust modulation classification …

… In our paper, we propose a deep learning-based AMC method that employs … deep learning
technology, Deep Belief Network (DBN), is applied for pattern recognition and classification. …

… Therefore, for the radio modulation classification task considered in this paper, rotating the
modulated radio signal is similar to rotating an image, without losing features for classification…

… methods in this study for automatic modulation classification (AMC). The traditional methods
usually separate feature extraction and the classification process. Meanwhile, they usually …

… Gui, “Data-driven deep learning for automatic modulation … Lightweightautomatic modulation
classification using deep learning … , “Deep neural network for robust modulation classification …