Automatic modulation recognition (AMR) detects the modulation scheme of the received
signals for further signal processing without needing prior information, and provides the …

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 …

Robustness against noise is critical for modulation recognition (MR) approaches deployed
in real-world communication systems. In MR systems, a corrupted signal is normally …

This article proposes an autoencoder-based method to enhance the information interaction
between in-phase/quadrature (I/Q) channels of the input data for automatic modulation …

Automatic modulation recognition (AMR) has been considered as an efficient technique for
non-cooperative communication and intelligent communication. In this work, we propose a …

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 (AMR) is a critical technology in spatial cognitive radio
(SCR), and building high-performance AMR model can achieve high classification accuracy …

In recent years, neural network algorithms have demonstrated tremendous potential for
modulation classification. Deep learning methods typically take raw signals or convert …

Automatic modulation classification (AMC), also termed blind signal modulation recognition,
plays a critical role in various civilian and military applications. Although existing …

Deep learning (DL)-based automatic modulation classification (AMC) has made
breakthroughs and is generally used for signal detection and recognition in wireless …