基于卷积神经网络的极化码译码算法

郭锐; 冉凡春

doi:10.11959/j.issn.1000-0801.2020130

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基于卷积神经网络的极化码译码算法

研究与开发 | 更新时间：2024-06-05

- 基于卷积神经网络的极化码译码算法
- Polar codes decoding algorithm based on convolutional neural network
- 电信科学 2020年36卷第6期页码：119-124
- 作者机构：
- 作者简介：
  
  [ "郭锐（1980- ），男，博士，杭州电子科技大学副教授、硕士生导师，主要从事无线通信、信道编码等方面的研究工作" ]
  [ "冉凡春（1995- ），男，杭州电子科技大学硕士生，主要研究方向为信道编码及深度学习" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020130
  中图分类号： TN911
- 网络出版日期：2020-06，
  
  纸质出版日期：2020-06-20
- 稿件说明：
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郭锐, 冉凡春. 基于卷积神经网络的极化码译码算法[J]. 电信科学, 2020,36(6):119-124.

Rui GUO, Fanchun RAN. Polar codes decoding algorithm based on convolutional neural network[J]. Telecommunications science, 2020, 36(6): 119-124.
郭锐, 冉凡春. 基于卷积神经网络的极化码译码算法[J]. 电信科学, 2020,36(6):119-124. DOI： 10.11959/j.issn.1000-0801.2020130.

Rui GUO, Fanchun RAN. Polar codes decoding algorithm based on convolutional neural network[J]. Telecommunications science, 2020, 36(6): 119-124. DOI： 10.11959/j.issn.1000-0801.2020130.

摘要

针对现有基于神经网络的极化码译码算法仅能对短码进行译码的问题（码长 N≤64），提出针对极化码长码（N≥512）的卷积神经网络译码算法，利用神经网络epoch和batch参数来调控神经网络的输入，而不是固定从数据样本中抽取一定比例用作训练集和测试集，解决了由码字过长造成的数据爆炸问题。此外，研究了batch和epoch参数对卷积神经网络译码性能的影响及神经网络使用不同激活函数的性能差异。仿真结果表明，与传统SCL（successive cancellation list，串行抵消列表）译码算法比较，卷积神经网络在低信噪比下取得略优于SCL（L=2）的性能，在高信噪比下取得与SCL（L=2）算法相近的性能，并且训练数据集越大，神经网络译码性能越好。

Abstract

In order to solve the problem that the existing Polar code decoding algorithm based on neural network can only decode short codewords (codewords length N≤64)

a new decoding algorithm using convolution neural network for long codewords (N≥512) was put forward.Instead of using a fixed drawn from the data sample proportion as training set and test set

the neural network parameters epoch and batch were used to control the neural network input

and the data explosion problem caused by the long codewords was solved.In addition

the influence of batch and epoch parameters on the decoding performance of convolution neural network was explored and the performance difference of neural network using different activation functions were investigated.Simulation results show that with the traditional SCL (successive cancellation list

L=2) decoding algorithm

convolution neural network in low signal-to-noise ratio on the performance is better than that of SCL (L=2)

in high signal-to-noise ratio and SCL (L=2) algorithm of similar performance

and the larger the training data set

the better the decoding performance of neural network.

关键词

Keywords

references

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