通信学报 ›› 2024, Vol. 45 ›› Issue (5): 70-79.doi: 10.11959/j.issn.1000-436x.2024097

• 学术论文 • 上一篇    下一篇

基于改进型LBP译码的LDPC码稀疏校验矩阵重建

张天骐, 李春运(), 吴仙越, 吴云戈   

  1. 重庆邮电大学通信与信息工程学院,重庆 400065
  • 收稿日期:2023-12-29 修回日期:2024-04-23 出版日期:2024-05-30 发布日期:2024-06-24
  • 通讯作者: 李春运 E-mail:1874791327@qq.com
  • 作者简介:张天骐(1971- ),男,四川眉山人,博士,重庆邮电大学教授、博士生导师,主要研究方向为通信信号的调制解调、盲处理、语音信号处理、神经网络实现以及FPGA、VLSL实现。
    李春运(2000- ),男,河南信阳人,重庆邮电大学硕士生,主要研究方向为信道编码参数盲识别。
    吴仙越(2000- ),女,重庆人,重庆邮电大学硕士生,主要研究方向为扩频信号盲估计。
    吴云戈(2000- ),女,河南许昌人,重庆邮电大学硕士生,主要研究方向为通信信号盲处理、深度学习。
  • 基金资助:
    重庆市自然科学基金资助项目(cstc2021jcyj-msxmX0836)

Reconstruction of LDPC code sparse check matrix based on modified LBP decoding

Tianqi ZHANG, Chunyun LI(), Xianyue WU, Yunge WU   

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Received:2023-12-29 Revised:2024-04-23 Online:2024-05-30 Published:2024-06-24
  • Contact: Chunyun LI E-mail:1874791327@qq.com
  • Supported by:
    The Natural Science Foundation of Chongqing(cstc2021jcyj-msxmX0836)

摘要:

针对LDPC码稀疏校验矩阵重建问题,基于改进型LBP译码的思想提出了一种高误码率下的LDPC码稀疏校验矩阵重建算法。首先,从码字矩阵中随机抽取部分比特构建码字分析矩阵,并对其做高斯消元求对偶空间;其次,通过判定对偶空间向量是否稀疏,提高了后续疑似校验向量判定的效率;最后,在接收码字个数不足时,利用已知校验向量结合改进型LBP译码方法纠正错误码字,加快LDPC码稀疏校验矩阵的重建速度,提高重建性能。仿真结果表明,所提算法在高误码率0.004 5的条件下,对于IEEE 802.11n协议下的(648,324)LDPC码,相比于现有算法,稀疏校验矩阵重建率提升了52.16%,可达到92.28%。

关键词: 低密度奇偶校验码, 稀疏校验矩阵, 高斯消元, 改进型LBP译码, 重建

Abstract:

In order to reconstruct the sparse check matrix of LDPC code, a sparse check matrix reconstruction algorithm for LDPC code at high BER was proposed based on modified LBP decoding. Firstly, some bits were selected randomly from the codeword matrix to construct the codeword analysis matrix, and Gaussian elimination on it was performed to find the dual space. Secondly, by determining whether the pairwise space vectors were sparse or not, it improved the efficiency of the subsequent suspected check vectors determination. Finally, in the case of insufficient received codes, the known check vectors were combined with the modified LBP decoding method to correct the wrong codes, so as to speed up the reconstruction of the sparse check matrix of LDPC code and improve the reconstruction performance. The simulation results show that the reconstruction rate of sparse check matrix of (648,324) LDPC codes in IEEE 802.11n protocol is improved by 52.16% compared with the existing algorithms, and can reach 92.28% at high BER of 0.004 5.

Key words: LDPC, sparse check matrix, Gaussian elimination, modified LBP decoding, reconstruction

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