LDPC Parameter Estimation by Matrices Product and Rank Analysis Under the Condition of Small Sampling

LIU Qian; ZHANG Hao; SONG Ying-jiong; WANG Gang

doi:10.12263/DZXB.20210485

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LDPC Parameter Estimation by Matrices Product and Rank Analysis Under the Condition of Small Sampling

PAPERS | 更新时间：2025-12-08

- LDPC Parameter Estimation by Matrices Product and Rank Analysis Under the Condition of Small Sampling
- ACTA ELECTRONICA SINICA Vol. 50, Issue 5, Pages: 1075-1082(2022)
- 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：
- 基金信息：
- DOI：10.12263/DZXB.20210485
  CLC： TN911.6
- Received：15 April 2021，
  
  Revised：2021-06-29，
  
  Published：25 May 2022
- 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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