基于随机抽取的稀疏校验矩阵重建算法

doi:10.12305/j.issn.1001-506X.2023.04.31

摘要/Abstract

摘要：

为了更好地实现高误码条件下稀疏校验矩阵的重建, 将寻找校验向量的过程转换为求解线性方程组的过程, 充分利用稀疏校验矩阵的稀疏性, 通过寻找包含所有校验比特的子线性方程组的解来重建稀疏校验矩阵, 并利用该矩阵对码字进行软译码, 以纠正错误码字, 对纠正后的码字继续重建稀疏校验矩阵, 不断重复该过程, 选取所有迭代过程中重建率最高的稀疏校验矩阵作为最终结果。仿真结果表明, 提出的稀疏校验矩阵算法可以在高误码条件下有效重建双对角结构及非双对角结构的稀疏校验矩阵, 对误码的鲁棒性较强。

关键词: 低密度奇偶校验, 线性方程组, 软判决译码

Abstract:

In order to better realize the reconstruction of sparse check matrix under the condition of high bit error, the process of finding check vectors is converted into the process of solving linear equations, making full use of the sparseness of sparse check matrix. The sparse check matrix is firstly reconstructed by the solution of the sub-linear equation system containing all check bits, and the matrix is used to soft-decode the codeword to correct the erroneous codeword, and continue to reconstruct the sparse check matrix for the corrected codeword. The process is repeated continuously, and the sparse check matrix with the highest reconstruction rate in all the iterative processes is selected as the final result. The simulation results show that the proposed sparse check matrix algorithm can effectively reconstruct the sparse check matrix of bidiagonal structure and non-bidiagonal structure under the condition of high bit error, and has strong robustness to bit errors.

Key words: low-density parity-check (LDPC), linear equations, soft decision decoding

中图分类号:

TN92

刘恒燕, 张立民, 闫文君, 谭凯文, 张聿远. 基于随机抽取的稀疏校验矩阵重建算法[J]. 系统工程与电子技术, 2023, 45(4): 1215-1221.

Hengyan LIU, Limin ZHANG, Wenjun YAN, Kaiwen TAN, Yuyuan ZHANG. Reconstruction algorithm of sparse check matrix based on random extraction[J]. Systems Engineering and Electronics, 2023, 45(4): 1215-1221.

图/表 5

参考文献 30

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