Deep Learning for Channel Decoding Under Correlated Noise

Xiangxiang Zhang, Tao Luo

2019

Abstract

Traditional Viterbi decoding algorithm degrades performance under correlated noise. Inspired by the powerful learning from data of deep neural networks, we apply denoising convolutional neural network (DnCNN) to channel decoding under correlated noise. In this paper, we propose a DnCNN hard decision Viterbi decoder (short for DHDV decoder) architecture to enhance the performance of convolutional codes under correlated noise. The architecture applies DnCNN to denoise and improves the decoding SNR (signal to noise ratio) through the correlation of noise correlation. Then, the Viterbi Algorithm decoder decodes from the denoised data. DHDV decoder obtains greater BER performance improvement under stronger noise correlation, and it is robust under different convolutional codes and correlated noise models. Comparing the complexity of DnCNN network and matrix multiplication whitening method, DnCNN network complexity is lower when the code length is longer.

Paper Citation

in Harvard Style

Zhang X. and Luo T. (2019). Deep Learning for Channel Decoding Under Correlated Noise.In Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC, ISBN 978-989-758-357-5, pages 135-141. DOI: 10.5220/0008098301350141

in Bibtex Style

@conference{ctisc19,
author={Xiangxiang Zhang and Tao Luo},
title={Deep Learning for Channel Decoding Under Correlated Noise},
booktitle={Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC,},
year={2019},
pages={135-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008098301350141},
isbn={978-989-758-357-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications - Volume 1: CTISC,
TI - Deep Learning for Channel Decoding Under Correlated Noise
SN - 978-989-758-357-5
AU - Zhang X.
AU - Luo T.
PY - 2019
SP - 135
EP - 141
DO - 10.5220/0008098301350141