A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization
We propose a generalization belief propagation (BP) decoding algorithm based on particle swarm optimization (PSO) to improve the performance of the polar codes. Through the analysis of the existing BP decoding algorithm, we first introduce a probability modifying factor to each node of the BP decode...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2014/606913 |
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doaj-3667101c32d24d49b7e186fbdd4242e12020-11-24T23:14:29ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472014-01-01201410.1155/2014/606913606913A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm OptimizationYingxian Zhang0Aijun Liu1Xiaofei Pan2Shi He3Chao Gong4Key Laboratory of Military Satellite Communications, College of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, ChinaKey Laboratory of Military Satellite Communications, College of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, ChinaKey Laboratory of Military Satellite Communications, College of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, ChinaUnit 75706, PLA, Guangzhou 510000, ChinaKey Laboratory of Military Satellite Communications, College of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, ChinaWe propose a generalization belief propagation (BP) decoding algorithm based on particle swarm optimization (PSO) to improve the performance of the polar codes. Through the analysis of the existing BP decoding algorithm, we first introduce a probability modifying factor to each node of the BP decoder, so as to enhance the error correcting capacity of the decoding. Then, we generalize the BP decoding algorithm based on these modifying factors and drive the probability update equations for the proposed decoding. Based on the new probability update equations, we show the intrinsic relationship of the existing decoding algorithms. Finally, in order to achieve the best performance, we formulate an optimization problem to find the optimal probability modifying factors for the proposed decoding algorithm. Furthermore, a method based on the modified PSO algorithm is also introduced to solve that optimization problem. Numerical results show that the proposed generalization BP decoding algorithm achieves better performance than that of the existing BP decoding, which suggests the effectiveness of the proposed decoding algorithm.http://dx.doi.org/10.1155/2014/606913 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yingxian Zhang Aijun Liu Xiaofei Pan Shi He Chao Gong |
spellingShingle |
Yingxian Zhang Aijun Liu Xiaofei Pan Shi He Chao Gong A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization Mathematical Problems in Engineering |
author_facet |
Yingxian Zhang Aijun Liu Xiaofei Pan Shi He Chao Gong |
author_sort |
Yingxian Zhang |
title |
A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization |
title_short |
A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization |
title_full |
A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization |
title_fullStr |
A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization |
title_full_unstemmed |
A Generalization Belief Propagation Decoding Algorithm for Polar Codes Based on Particle Swarm Optimization |
title_sort |
generalization belief propagation decoding algorithm for polar codes based on particle swarm optimization |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2014-01-01 |
description |
We propose a generalization belief propagation (BP) decoding algorithm based on particle swarm optimization (PSO) to improve the performance of the polar codes. Through the analysis of the existing BP decoding algorithm, we first introduce a probability modifying factor to each node of the BP decoder, so as to enhance the error correcting capacity of the decoding. Then, we generalize the BP decoding algorithm based on these modifying factors and drive the probability update equations for the proposed decoding. Based on the new probability update equations, we show the intrinsic relationship of the existing decoding algorithms. Finally, in order to achieve the best performance, we formulate an optimization problem to find the optimal probability modifying factors for the proposed decoding algorithm. Furthermore, a method based on the modified PSO algorithm is also introduced to solve that optimization problem. Numerical results show that the proposed generalization BP decoding algorithm achieves better performance than that of the existing BP decoding, which suggests the effectiveness of the proposed decoding algorithm. |
url |
http://dx.doi.org/10.1155/2014/606913 |
work_keys_str_mv |
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