The automatic modulation classification (AMC) of a detected signal has gained considerable
prominence in recent years owing to its numerous facilities. Numerous studies have focused …

This paper proposes a hybrid feature extraction convolutional neural network combined with
a channel attention mechanism (HFECNET-CA) for automatic modulation recognition …

The feature extraction of Automatic Modulation Classification (AMC) is difficult, and the
classification performance is poor, particularly for low SNRs and fading channels. To solve …

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) …

Automatic modulation classification (AMC) is the premise for signal detection and
demodulation applications, especially in non-cooperative communication scenarios. It has …

Automatic modulation classification (AMC) aims at identifying the modulation of the received
signals, which is a significant approach to identifying the target in military and civil …

Automatic modulation classification (AMC) is to identify the modulation type of a received
signal, which plays a vital role to ensure the physical-layer security for Internet of Things …

Deep learning (DL) has been used more and more in the field of automatic modulation
classification (AMC) in recent years, but there are still many areas for improvement. In this …

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 …

Automatic modulation classification (AMC) based on constellation diagrams that own distinct
modulation features performs well in Gaussian channels. However, proper constellation …