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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Bibliographic Details
Published in:Wireless communications and mobile computing Vol. 2021; no. 1
Main Authors: Yan, Ming, Lou, Xingrui, Wang, Yan
Format: Journal Article
Language:English
Published: Oxford Hindawi 2021
John Wiley & Sons, Inc
Subjects:
Algorithms
Artificial neural networks
Binary system
Channel noise
Codes
Coding
Communication
Correlation coefficients
Data processing
Decoding
Deep learning
Network reliability
Neural networks
Noise
Noise propagation
Optimization
Optimization algorithms
Wireless communications
ISSN:1530-8669, 1530-8677
Online Access:Get full text
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Summary: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.
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ISSN:1530-8669
1530-8677
DOI:10.1155/2021/1434347