The arrival of 5G has accelerated the development of the Internet of things and vehicular
technology, which often need to transmit large amounts of data through wireless networks …

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 …

In this paper, we propose an effective feature-based automatic modulation classification
(AMC) method using a deep neural network (DNN). In order to classify the modulation type …

In cognitive radio, signal recognition is an important technology and modulation recognition
plays a key role in it. With the development of artificial intelligence, deep learning algorithms …

Since the emergence of 5G technology, the wireless communication system has had a huge
data throughput, so the joint development of artificial intelligence technology and wireless …

Various tools and methods have been used in the problem of automatic modulation
classification (AMC) with success, such as maximum likelihood estimation (MLE), K-nearest …

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 …

The feature extraction of Automatic Modulation Classification (AMC) is difficult, and the
classification performance is poor, particularly for low SNRs and fading channels. To solve …

With the proliferation of frequency-using devices and the advent of the era of big data,
spectrum management and control are faced with challenges of effectiveness and accuracy …

The 5G and beyond wireless networks will be more dynamic and heterogeneous, which
needs to work on multistrand waveforms. One of the most significant challenges in such a …