Symbol denoising in high order M-QAM using residual learning of deep CNN

S Khan, KS Khan, SY Shin - 2019 16th IEEE Annual Consumer …, 2019 - ieeexplore.ieee.org
This paper presents an integrating concept of de-noising convolutional neural networks
(DnCNN) with quadrature amplitude modulation (QAM) for symbol denoising. DnCNN is …

Recurrent network with attention for symbol detection in communication systems

K Chia, VM Baskaran, KS Wong… - … on Intelligent Signal …, 2022 - ieeexplore.ieee.org
One major challenge for wireless receivers to maintain information fidelity involves the
demodulation of faded signals in noisy environments. Typical demodulation techniques for …

Efficient residual shrinkage CNN denoiser design for intelligent signal processing: Modulation recognition, detection, and decoding

L Zhang, X Yang, H Liu, H Zhang… - IEEE Journal on …, 2021 - ieeexplore.ieee.org
The noises embedded in signals will degrade the signal processing quality. Traditional
denoising algorithms might not work in practical systems since the statistical characteristics …

M-QAM demodulation based on machine learning

RN Toledo, C Akamine, F Jerji… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
This paper presents a new Quadrature Amplitude Modulation (M-QAM) demodulation
method using Machine Learning techniques. The new method significantly reduces the …

Demodulation of faded wireless signals using deep convolutional neural networks

AS Mohammad, N Reddy, F James… - 2018 IEEE 8th Annual …, 2018 - ieeexplore.ieee.org
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 …

[HTML][HTML] Emergence of deep learning as a potential solution for detection, recovery and de-noising of signals in communication systems

K Thakkar, A Goyal, B Bhattacharyya - International Journal of Intelligent …, 2020 - Elsevier
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 …

Simplified ANN for 256 QAM symbol equalization over OFDM Rayleigh channel

F Bouguerra, L Saidi - 2018 International Conference on Smart …, 2018 - ieeexplore.ieee.org
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 …

Combining deep learning and linear processing for modulation classification and symbol decoding

S Hanna, C Dick, D Cabric - GLOBECOM 2020-2020 IEEE …, 2020 - ieeexplore.ieee.org
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 …

A Machine Learning Approach for Simultaneous Demapping of QAM and APSK Constellations

A Gansekoele, A Balatsoukas-Stimming… - arXiv preprint arXiv …, 2024 - arxiv.org
As telecommunication systems evolve to meet increasing demands, integrating deep neural
networks (DNNs) has shown promise in enhancing performance. However, the trade-off …

BLDnet: Robust learning-based detection for high-order QAM with nonlinear distortion

L Zou, M Jiang, C Zhao, Y He, D Zhu… - 2020 IEEE/CIC …, 2020 - ieeexplore.ieee.org
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 …