… 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-…

… 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 …

… 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 …

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

… In this paper, we studied radio data augmentation methods for deep learning-based
modulation classification. Specifically, three typical augmentation methods, ie, rotation, flip, and …

… Deep learning (DL) is a … and modulation classification, which are significant in many
communication systems. In this work, a DL framework for multi-signals detection and modulation …

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

… Abstract—Automatic modulation classification (AMC) is one … deep learning model is designed
for AMC in CR. A convolutional neural network (CNN) is applied in both the deep learning …