Automatic modulation classification (AMC) can generally be divided into knowledge-based
methods and data-driven methods. In this paper, we explore combining the knowledge …

Automatic modulation classification (AMC) is an important part of signal identification for
cognitive radio as well as military communication. The problem has been approached …

Automatic modulation classification (AMC) identifies a received signal's modulation scheme
without prior knowledge of the intercepted signal, which enables significant applications in …

Automatic modulation classification (AMC), which aims to blindly identify the modulation type
of an incoming signal at the receiver in wireless communication systems, is a fundamental …

Automatic modulation classification (AMC) lies at the core of cognitive radio and spectrum
sensing. In this Letter, the authors propose a novel convolutional neural network (CNN) …

Automatic modulation classification (AMC) plays a key role in cognitive radio. For AMC,
convolutional neural networks (CNNs) have been explored in previous works extensively …

Deep learning (DL) has been recognized as an effective solution for automatic modulation
classification (AMC). However, most recent DL based AMC works are based on real-valued …

Deep neural networks have been recently applied in automatic modulation classification
task and achieved remarkable success. However, Existing neural networks mainly focus on …

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 …

In this paper, we propose MCformer-a novel deep neural network for the automatic
modulation classification task of complex-valued raw radio signals. MCformer architecture …