基于空间压缩的QC-LDPC码稀疏校验矩阵重建

doi:10.12305/j.issn.1001-506X.2025.10.33

Abstract

Abstract:

In order to reconstruct the sparse check matrix of quasi-cyclic low density parity check （QC-LDPC） code, a sparse check matrix reconstruction algorithm for QC-LDPC code at high bit error rate is proposed based on the idea of spatial compression. The QC-LDPC code is compressed according to the quasi-cyclic block length, and the check position matrix is reconstructed using the compressed code sequence. According to the position of “1” in the check position vector, the corresponding quasi-cyclic blocks are intercepted to form a new code space, and the sparse check matrix is reconstructed by using the iterative elimination method. During the reconstruction process, the methods of “eliminating wrong codes” and “modified layered belief propagation decoding” are used to improve the quality of codes, so as to accelerate the reconstruction speed and enhance the reconstruction performance. The simulation results show that the reconstruction rate of sparse check matrix of （648,324） LDPC code in IEEE 802.11n protocol is improved by 25.48%, and can reach 92.28% at high bit error rate of 0.004, it can provide a more complete checksum matrix for subsequent channel decoding.

Key words: quasi-cyclic low density parity check (QC-LDPC), sparse check matrix, spatial compression, modified layered belief propagation decoding

CLC Number:

TN 911.7

Chunyun LI, Tianqi ZHANG, Yunge WU, Xianyue WU. Reconstruction of QC-LDPC code sparse check matrix based on spatial compression[J]. Systems Engineering and Electronics, 2025, 47(10): 3504-3511.

Figures/Tables 9

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算法	复杂度
随机抽取算法	$O({{\mathrm{iter}}_{{\mathrm{col}}}} \cdot {{\mathrm{iter}}_{{\mathrm{row}}}} \cdot {s'^3})$
比特翻转算法	$O({{\mathrm{iter}}_{{\mathrm{col}}}} \cdot {{\mathrm{iter}}_{{\mathrm{row}}}} \cdot {s'^3})$
本文算法	$O({{\mathrm{iter}}_{{\mathrm{col}}}} \cdot {{\mathrm{iter}}_{{\mathrm{row}}}} \cdot {s^3} + {\mathrm{iter}} \cdot {({\mathrm{wt}} \cdot q)^3})$

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Reconstruction of QC-LDPC code sparse check matrix based on spatial compression

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