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 …

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 …

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 …

For spectral efficiency, higher order modulation symbols confer information on more than
one bit. As soft detection forward error correction decoders assume the availability of …

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 …

Following the great success of Machine Learning (ML), especially Deep Neural Networks
(DNNs), in many research domains in 2010s, several ML-based approaches were proposed …

Deep neural network (DNN)-based channel decoding is widely considered in the literature.
The existing solutions are investigated for the case of hard output, ie when the decoder …

The demodulation of digital signal plays a key role in the communication system. The
traditional demodulator is usually realized by special hardware platform, which has the …

With the flourishing development of wireless communication, further challenges will be
introduced by the future demands of emerging applications. However, in the face of more …

The article first presents the formulation for computing the optimum soft decision metrics for
decoding of convolutional codes on fading channels and then demonstrate the performance …