Automatic Modulation Classification (AMC) is the process of determining the modulation
scheme of an intercepted signal with no a priori information about its characteristics. AMC's …

Deep Neural Networks (DNNs) have achieved remarkable accuracy improvements for
automatic modulation classification. However, the employed networks often have millions of …

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

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 …

Deep learning (DL) nowadays activates the advancement of multiple research fields, from
signal processing to computer vision and communication. Compared with traditional …

Due to the evolution and availability of vast amounts of data for transferring, receiving, and
detection, the field of signal recognition and modulation classification has become vital in …

Automatic modulation classification (AMC) is an approach that can be leveraged to identify
an observed signal's most likely employed modulation scheme without any a priori …

In this paper, a multi-scale convolutional neural network-based (MSN) method is proposed
for robust automatic modulation classification (AMC). The classifier directly utilizes in-phase …

Automatic modulation classification (AMC) has been studied for more than a quarter of a
century; however, it has been difficult to design a classifier that operates successfully under …

Automatic modulation classification (AMC) is a vital process in wireless communication
systems that is fundamentally a classification problem. It is employed to automatically …