基于余弦校验关系的卷积神经网络LDPC码盲识别

doi:10.12305/j.issn.1001-506X.2025.09.34

Abstract

Abstract:

Aiming at the problem of low recognition rate of low density parity check （LDPC） code in low signal-to-noise ratio environment, a convolutional neural network （CNN） algorithm based on the distribution of cosine check relations hip is proposed. This algorithm utilizes the statistical distribution differences of cosine check relations hip between codewords and correct and incorrect check matrices. The statistical characteristics of cosine check relations hip calculated from LDPC codes and candidate set check matrices are used as inputs for the CNN. By leveraging the deep information mining capability of CNN, a simple four-layer CNN model is designed to achieve effective recognition of LDPC codes. Simulation results show that with only one codeword, the correct recognition rate of LDPC codes with code rates of 1/2, 2/3B, 3/4A, 3/4B, and 5/6, and a code length of 2304, exceeds 90% at a signal-to-noise ratio of 3.25 dB. Compared to traditional algorithms, performance is improved by 0.25?1.25 dB.

Key words: low density parity check (LDPC) code, closed set recognition, cosine check relationship, convolutional neural network (CNN)

CLC Number:

TN 914.34

Wenjie CHEN, Pu ZHANG, Gaoxiang SHI, Lin LIU, Xuan LIU. Blind recognition of LDPC codes based on the convolutional neural network with cosine check relationship[J]. Systems Engineering and Electronics, 2025, 47(9): 3117-3125.

Figures/Tables 10

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References 30

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类型	超参数
优化器	Adam
学习率	0.001
批量大小	128
激活函数	Relu
迭代次数	20
填充方式	same

算法	时间
基于SPP最大值算法	7.81
基于PPLLR均值最大值算法	7.11
基于最大余弦比算法	7.37
基于KL散度最大值算法	4.72
本文CNN算法	3.96

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Blind recognition of LDPC codes based on the convolutional neural network with cosine check relationship

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