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

Considering the problem that the traditional noise reduction algorithm damages the high
Signal-to-Noise Ratio (SNR) signal and reduces the accuracy of signal recognition, a SNR …

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 …

Automatic modulation classification plays an important role in many fields to identify the
modulation type of wireless signals in order to recover signals by demodulation. In this …

Modulation recognition (MR) is an important technology in modern communication systems.
Traditional MR methods can be categorised as the maximum likelihood hypothesis, pattern …

With the rapid development of modern communication technology, it has become a core
problem in the field of communication to find new ways to effectively modulate signals and to …

Automatic modulation classification (AMC), which plays a significant role in wireless
communication, can recognize the modulation type of the received signal without large …

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 …

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 …

With the advancement of 5G technology, wireless communication resources such as
channels and spectrum become scarce. This necessitates ensuring the efficiency and …