In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and
establish the first open dataset of real modulated signals for wireless communication …

In wireless communication, signal demodulation under non-ideal conditions is one of the
important research topic. In this paper, a novel non-coherent binary phase shift keying …

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 …

In a non-cooperative communication environment, automatic modulation classification
(AMC) is an essential technology for analyzing signals and classifying different kinds of …

In this paper we presented a neural network-based method to demodulate digital signals.
After training with different modulation schemes, the learning-based receiver can perform …

This paper demonstrates exceptional performance of approximately 10.0 dB learning-based
gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh …

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 …

Recently, deep learning (DL) based automatic modulation classification (AMC) has received
much attention. Various network structures with higher complexity are utilized to boost the …

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

In this letter, a multi-task deep convolutional neural network, namely MoDANet, is proposed
to perform modulation classification and DOA estimation simultaneously. In particular, the …