Deep learning (DL) classifiers have significantly outperformed traditional likelihood-based or
feature-based classifiers for signal modulation recognition in non-cooperative environments …

Automatic modulation classification (AMC), which aims to identify the modulation types of
unknown signals without any prior knowledge, plays a key role in intelligent wireless …

Due to the influence of noise in the received signal in non-cooperative communication, it is
difficult for existing Automatic modulation classification methods to balance classification …

The modulation format is a key parameter that influences the monitoring of the intercepted
signals. Automatic modulation classification (AMC) is utilized to recognize the modulation …

Modulation recognition plays an indispensable role in the field of wireless communications.
In this paper, a novel attention cooperative framework based on deep learning is proposed …

For modulation classification, hand-crafted approaches can generalize well from a few
samples, yet deep learning algorithms require millions of samples to achieve the superior …

Traditional denoising algorithms are easy to lose signal details, resulting in low recognition
accuracy of modulated signals. A modulation signal classification algorithm based on …

Following the great success of deep learning in signal processing, Many models based on
real-valued convolutional neural networks (CNNs) and recurrent neural networks (RNNs) …

Specific emitter identification (SEI) is a promising technology to discriminate the individual
emitter and enhance the security of various wireless communication systems. SEI is …

Automatic Modulation Classification (AMC) is a challenging yet significant technique for
communication systems. Deep learning methods, though widely employed for AMC, are …