首页 | 官方网站   微博 | 高级检索  
     

基于CNN扰动的极化码译码算法
引用本文:赵生妹, 徐鹏, 张南, 孔令军. 基于CNN扰动的极化码译码算法[J]. 电子与信息学报, 2021, 43(7): 1900-1906. doi: 10.11999/JEIT200136
作者姓名:赵生妹  徐鹏  张南  孔令军
作者单位:1.南京邮电大学通信与信息工程学院 南京 210003;;2.中国航天系统科学与工程研究院 北京 100048
基金项目:国家自然科学基金(61871234, 11847062);中国博士后科学基金(2020M671595);江苏省博士后科研资助计划(2020Z198);南京邮电大学国自孵化基金 (NY219075)
摘    要:针对中短码长下串行抵消(SC)算法性能较差,且串行抵消列表(SCL)算法复杂度较高等问题,根据译码纠错空间理论,该文提出了一种基于卷积神经网络(CNN)扰动的极化码译码算法.对SC译码失败的接收序列,通过CNN产生相应的扰动噪声,并将该扰动噪声添加到接收信号中,然后根据重新计算的似然信息进行译码.仿真结果表明:与SC译...

关 键 词:极化码  串行抵消译码  扰动噪声  卷积神经网络
收稿时间:2020-02-28
修稿时间:2020-11-30

A Decoding Algorithm of Polar Codes Based on Perturbation with CNN
Shengmei ZHAO, Peng XU, Nan ZHANG, Lingjun KONG. A Decoding Algorithm of Polar Codes Based on Perturbation with CNN[J]. Journal of Electronics & Information Technology, 2021, 43(7): 1900-1906. doi: 10.11999/JEIT200136
Authors:Shengmei ZHAO  Peng XU  Nan ZHANG  Lingjun KONG
Affiliation:1. Institute of Signal Processing&Transmission, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;;2. China Aerospace Academy of Systems Science and Engineering, Beijing 100048, China
Abstract:According to the space theory for error correction, a Polar decoding algorithm for medium and short code lengths, based on the perturbation with a Convolution Neural Network (CNN), is presented to overcome the poor performance of the Successive Cancellation (SC) decoding algorithm and the high complexity of the Successive Cancellation List (SCL) decoding algorithm. For any receiving signals that failing to decode, a perturbation noise, generated through the CNN, is added to the receiving signal, and the likelihood information is then recalculated for further decoding. The simulation results show that the proposed algorithm has a gain of about 0.6 dB compared with the SC decoding algorithm, and an improvement of about 0.1 dB and a lower average complexity than that of SCL decoding algorithm when L=16.
Keywords:Polar code  Successive Cancellation (SC) decoding  Perturbation noise  Convolutional Neural Network (CNN)
点击此处可从《电子与信息学报》浏览原始摘要信息
点击此处可从《电子与信息学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号