Signal demodulation in short range multi-path channel plays an important role in
communication system. The existed wireless communication system in short range multi …

Since the emergence of 5G technology, the wireless communication system has had a huge
data throughput, so the joint development of artificial intelligence technology and wireless …

The modulation recognition of digital signals under non-cooperative conditions is one of the
important research contents here. With the rapid development of artificial intelligence …

This paper presents an integrating concept of de-noising convolutional neural networks
(DnCNN) with quadrature amplitude modulation (QAM) for symbol denoising. DnCNN is …

In this letter, we introduce CGDNet, a cost-efficient hybrid neural network composed of a
shallow convolutional network, a gated recurrent unit, and a deep neural network, for robust …

With the development of artificial intelligence technology, deep learning has been applied to
automatic modulation classification (AMC) and achieved very good results. In this paper, we …

In this paper an artificial neural network demodulator to demodulate binary frequency shift
keying signal is proposed. This demodulator has some important features compared with …

We study the robustness of modulation recognition using deep neural networks. This is of
critical importance for applying deep learning for radio modulation classification, because …

Modulation classification is one of the key tasks for communications systems monitoring,
management, and control for addressing technical issues, including spectrum awareness …

Automatic modulation classification (AMC) enables significant applications in both the
military and civilian domains. Inspired by the great success of deep learning (DL), a dual …