Federated learning (FL) has become an increasingly popular solution for intrusion detection
to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based …

Due to the computing capability and memory limitations, it is difficult to apply the traditional
deep learning (DL) models to the edge devices (EDs) for realizing lightweight automatic …

Automatic modulation classification (AMC) is a key step of signal demodulation that
determines whether the receiver can correctly receive the transmitted signal without prior …

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 …

This paper introduces a deep learning (DL)-based Automatic Modulation Classification
(AMC) model. Our model is considered to be a receiver with a modulation classifier that is …

Due to the shortage of radio spectrum in the current 5G and upcoming 6G systems, the
cognitive radio (CR) technique is indispensable for spectrum management and can put the …

Automatic modulation classification (AMC) is one of the major challenges for cognitive radio
(CR), which can enhance the spectrum utilization efficiency. In this study, a hybrid signal and …

In a non-cooperative communication environment, automatic modulation classification
(AMC) is an essential technology for analyzing signals and classifying different kinds of …

Automatic modulation recognition plays an important role in military and civilian
applications, identifying the modulation format of received signals before signal …

A scalable and computationally efficient framework is designed to fingerprint real-world
Bluetooth devices. We propose an embedding-assisted attentional framework (Mbed-ATN) …