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

One major challenge for wireless receivers to maintain information fidelity involves the
demodulation of faded signals in noisy environments. Typical demodulation techniques for …

The noises embedded in signals will degrade the signal processing quality. Traditional
denoising algorithms might not work in practical systems since the statistical characteristics …

This paper presents a new Quadrature Amplitude Modulation (M-QAM) demodulation
method using Machine Learning techniques. The new method significantly reduces the …

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 …

Increase in communication devices demand for more intelligent and robust communication
systems. In this work, we present our initial research on various aspects of combining deep …

Increasing the specter efficiency has been an object for many studies. In this paper, we
investigate the higher modulation 256 QAM using Artificial Neural Networks (ANN) as an …

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 …

As telecommunication systems evolve to meet increasing demands, integrating deep neural
networks (DNNs) has shown promise in enhancing performance. However, the trade-off …

The performances of wireless communication systems are strongly limited by the
nonlinearities that exist in the transceiver. We first study the effect of nonlinearities of power …