脉冲信道下基于深度学习的BP译码方法

doi:10.3969/j.issn.1001-506X.2020.09.30

Abstract

Abstract:

Aiming at the poor decoding performance of the belief propagation (BP) algorithm for low-density parity-check (LDPC) codes with short block lengths, a BP decoding method based on deep learning is presented in impulsive channels. Firstly, two deep neural network models are constructed by the Tanner graph, and the weights of edges in the Tanner graph are reassigned to improve the decoding performance. Then, the calculation method of the log-likelihood ratio (LLR) of the channel is simplified, the model is trained to optimize approximate calculation parameters, and the decoding model robust to parameter γ is obtained. Finally, a robust training set is constructed to obtain the decoding model robust to parameter α and γ. Simulation results show that, at a high code rate, the performance of the proposed method is significantly improved compared with traditional BP algorithms. Furthermore, when approximately calculating the LLR value of the channel, the decoding performance is robust in impulsive channels with different parameters.

Key words: deep learning, impulsive channel, channel decoding, low-density parity-check (LDPC) code

CLC Number:

TN911.7

Rui PAN, Lei YUAN. BP decoding method based on deep learning in impulsive channels[J]. Systems Engineering and Electronics, 2020, 42(9): 2116-2122.

Figures/Tables 13

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References 19

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实验编号	深度神经网络模型的网络层	权重数量	优化算法	学习率	mini-batch
1	输入层、准确LLR层、5个变量层、5个校验层、5个判决层	5(3h+n)	RMSProp	0.01	50
1	输入层、准确LLR层、循环层、判决层	3h+n
2	输入层、近似LLR层、循环层、判决层	3h+n+2
3	输入层、近似LLR层、循环层、判决层	3h+n+2

码字类型	R	α值	(E_b/N₀)/dB
全零码字	1/2	1	{9, 10, 11, 12, 13, 14}
	1/2	1.5	{6, 7, 8, 9, 10, 11}
	3/4、4/5	1	{20, 22, 24, 26, 28, 30}
	3/4、4/5	1.5	{14, 16, 18, 20, 22, 24}

R	码字类型	α值	E_b/N₀/dB	数量
3/4	全零码字	0.8，1，1.5	{20, 22, 24}	270 000
1/2	全零码字	0.8，1，1.5	{9, 10, 11}	270 000

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BP decoding method based on deep learning in impulsive channels

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