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

Modulation identification of the transmitted signals remain a challenging area in modern
intelligent communication systems like cognitive radios. The computation of the distinct …

Automatic modulation classification plays an important role in many fields to identify the
modulation type of wireless signals in order to recover signals by demodulation. In this …

Automatic modulation classification facilitates many important signal processing
applications. Recently, deep learning models have been adopted in modulation recognition …

Automatic modulation classification (AMC) is becoming increasingly important for its
fundamental role in dynamic spectrum access, which can support 5G wireless …

Recently, deep learning has received a lot of attention in many machine learning
applications for its superior classification performance in speech recognition, natural …

As a backbone of deep learning models, convolutional neural networks (CNNs) are widely
used in the field of automatic modulation classification. Nevertheless, we speculate that the …

Automatic modulation recognition (AMR) is a promising technology for intelligent
communication receivers to detect signal modulation schemes. Recently, the emerging deep …

During the last few years, automatic modulation classification (AMC) has attracted
widespread attention in both civilian and military applications. Conventional AMC schemes …

In this paper, we propose a deep neural network (DNN)-based automatic modulation
classification (AMC) for digital communications. While conventional AMC techniques …