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 …

In recent years, deep learning is becoming more and more popular. It has been widely
applied to fields including image recognition, automatic speech recognition and natural …

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 …

Soft demodulation is a basic module of traditional communication receivers. It converts
received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the non-ideal …

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

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 …

Demodulation techniques are of central importance for achieving intelligent receiving.
Improvement in demodulation performance enhances the overall performance of a …

Deep learning has been recently applied to many problems in wireless communications
including modulation classification and symbol decoding. Many of the existing end-to-end …