小样本条件下基于矩阵乘法和秩分析的LDPC参数估计方法

刘倩; 张昊; 宋莹炯; 王刚

doi:10.12263/DZXB.20210485

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小样本条件下基于矩阵乘法和秩分析的LDPC参数估计方法

学术论文 | 更新时间：2025-12-08

- 小样本条件下基于矩阵乘法和秩分析的LDPC参数估计方法
- LDPC Parameter Estimation by Matrices Product and Rank Analysis Under the Condition of Small Sampling
- 电子学报 2022年50卷第5期页码：1075-1082
- 作者机构：
  
  1.信息工程大学信息系统工程学院, 河南郑州 450001
  2.信息工程大学密码工程学院, 河南郑州 450001
- 作者简介：
  
  [ "刘　倩　女,1984年10月出生于河北省任丘市.在读博士，信息工程大学副教授.主要研究方向为信道编码的识别分析. E⁃mail: liuqian2006815@126.com" ]
  [ "张　昊　男, 1984年3月出生于河南省郑州市.博士.现为信息工程大学讲师.主要研究方向为傅里叶分析、信息隐藏和信道编码. E⁃mail: haozhang78@126.com" ]
  宋莹炯男, 1996年5月出生于浙江省绍兴市. 现为信息工程大学密码工程学院本科生. E⁃mail: 2768730013@qq.com
  王刚男, 1980年出生于安徽省滁州市. 博士. 现为信息工程大学讲师. 主要研究方向为信源编码. E⁃mail: phzttyw@126.com
- 基金信息：
  
  国家自然科学基金(61802430;62072057);博士后科学基金(2016M603035)
- DOI：10.12263/DZXB.20210485
  中图分类号： TN911.6
- 收稿：2021-04-15，
  
  修回：2021-06-29，
  
  纸质出版：2022-05-25
- 稿件说明：
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刘倩,张昊,宋莹炯等.小样本条件下基于矩阵乘法和秩分析的LDPC参数估计方法[J].电子学报,2022,50(05):1075-1082.

LIU Qian,ZHANG Hao,SONG Ying-jiong,et al.LDPC Parameter Estimation by Matrices Product and Rank Analysis Under the Condition of Small Sampling[J].ACTA ELECTRONICA SINICA,2022,50(05):1075-1082.
刘倩,张昊,宋莹炯等.小样本条件下基于矩阵乘法和秩分析的LDPC参数估计方法[J].电子学报,2022,50(05):1075-1082. DOI： 10.12263/DZXB.20210485.

LIU Qian,ZHANG Hao,SONG Ying-jiong,et al.LDPC Parameter Estimation by Matrices Product and Rank Analysis Under the Condition of Small Sampling[J].ACTA ELECTRONICA SINICA,2022,50(05):1075-1082. DOI： 10.12263/DZXB.20210485.

摘要

在非合作通信背景下，利用传统的盲识别算法获取有用信息往往需要大量的截获数据.本文利用少量的截获数据，基于码字空间与其对偶空间的正交性、完整码字比特间的线性相关性和矩阵乘积秩的性质，提出了矩乘秩减算法

在无误码和低误码率情形下恢复了LDPC (Low-Density Parity-Check)长码的码长和起点.仿真实验表明

与传统算法相比，达到同样的识别效果本文算法能够节省至少20%的数据量，且运算量没有明显增加.

Abstract

In the scenario of non-cooperative communications

usually it takes a large amount of intercepted data for blind identification to obtain useful information with the traditional methods. This paper presents an approach called rank reduction with matrices production to estimate block length and synchronization of long LDPC(Low-Density Parity-Check) codes under the condition of small sampling with noise-free or lower bit-error rate data. Our method is based on the orthogonality of codeword space and its dual space

the linear correlations among the bits in a whole codeword

and the property of rank reduction of matrices. Experimental results show that

our method can save 25% data at least to reach the same identification probability compared with the traditional methods

and the computation has no obvious increasement.

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references

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