Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network

Polar code has the characteristics of simple coding and high reliability, and it has been used as the control channel coding scheme of 5G wireless communication. However, its decoding algorithm always encounters problems of large decoding delay and high iteration complexity when dealing with channel...

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Autores principales:	Ming Yan, Xingrui Lou, Yan Wang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/ff7d20c6db4b426ba98750e49f0b45b7
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id	oai:doaj.org-article:ff7d20c6db4b426ba98750e49f0b45b7
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spelling	oai:doaj.org-article:ff7d20c6db4b426ba98750e49f0b45b72021-11-22T01:11:30ZChannel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network1530-867710.1155/2021/1434347https://doaj.org/article/ff7d20c6db4b426ba98750e49f0b45b72021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1434347https://doaj.org/toc/1530-8677Polar code has the characteristics of simple coding and high reliability, and it has been used as the control channel coding scheme of 5G wireless communication. However, its decoding algorithm always encounters problems of large decoding delay and high iteration complexity when dealing with channel noise. To address the above challenges, this paper proposes a channel noise optimized decoding scheme based on a convolutional neural network (CNN). Firstly, a CNN is adopted to extract and train the colored channel noise to get more accurate estimation noise, and then, the belief propagation (BP) decoding algorithm is used to decode the polar codes based on the output of the CNN. To analyze and verify the performance of the proposed channel noise optimized decoding scheme, we simulate the decoding of polar codes with different correlation coefficients, different loss function parameters, and different code lengths. The experimental results show that the CNN-BP concatenated decoding can better suppress the colored channel noise and significantly improve the decoding gain compared with the traditional BP decoding algorithm.Ming YanXingrui LouYan WangHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Ming Yan Xingrui Lou Yan Wang Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network
description	Polar code has the characteristics of simple coding and high reliability, and it has been used as the control channel coding scheme of 5G wireless communication. However, its decoding algorithm always encounters problems of large decoding delay and high iteration complexity when dealing with channel noise. To address the above challenges, this paper proposes a channel noise optimized decoding scheme based on a convolutional neural network (CNN). Firstly, a CNN is adopted to extract and train the colored channel noise to get more accurate estimation noise, and then, the belief propagation (BP) decoding algorithm is used to decode the polar codes based on the output of the CNN. To analyze and verify the performance of the proposed channel noise optimized decoding scheme, we simulate the decoding of polar codes with different correlation coefficients, different loss function parameters, and different code lengths. The experimental results show that the CNN-BP concatenated decoding can better suppress the colored channel noise and significantly improve the decoding gain compared with the traditional BP decoding algorithm.
format	article
author	Ming Yan Xingrui Lou Yan Wang
author_facet	Ming Yan Xingrui Lou Yan Wang
author_sort	Ming Yan
title	Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network
title_short	Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network
title_full	Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network
title_fullStr	Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network
title_full_unstemmed	Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network
title_sort	channel noise optimization of polar codes decoding based on a convolutional neural network
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/ff7d20c6db4b426ba98750e49f0b45b7
work_keys_str_mv	AT mingyan channelnoiseoptimizationofpolarcodesdecodingbasedonaconvolutionalneuralnetwork AT xingruilou channelnoiseoptimizationofpolarcodesdecodingbasedonaconvolutionalneuralnetwork AT yanwang channelnoiseoptimizationofpolarcodesdecodingbasedonaconvolutionalneuralnetwork
_version_	1718418319677063168

Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network

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