Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (4): 1136-1143.doi: 10.13229/j.cnki.jdxbgxb.20221526

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Improved algorithm based on self-corrected min-sum decoding for LDPC codes

Hong JIANG(),Hong-liang XU   

  1. College of Information Engineering，Southwest University of Science and Technology，Mianyang 621000，China
  • Received:2022-11-29 Online:2024-04-01 Published:2024-05-17

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Abstract:

Low density parity check code is a kind of linear block code. Due to the low content of non-zero elements， there is a problem that the node information reliability determination is not accurate enough in the decoding process. Therefore， an improved LDPC decoding algorithm based on self correcting minimum sum is proposed. The iteration rule of BP decoding algorithm is analyzed. The first minimum and the second minimum of the minimum sum algorithm are used to set the correction threshold of variable node information， and the self-corrected min-sum algorithm is improved. The order statistics theory is used to obtain the normalization factor corresponding to the two minimum values to prevent the transmission and diffusion of unreliable variable node information in the iterative decoding process. Simulation results show that when the bit error rate is 10-5， the proposed algorithm can obtain about 0.2 dB of decoding performance gain， and the average number of iterations can be reduced by 18.2% at most， which proves that the proposed algorithm can effectively improve the decoding performance and iterative convergence performance.

Key words: LDPC code, self-corrected min-sum algorithm, modified threshold, order statistic, belief propagation algorithm

CLC Number: 

  • TN911.22

Fig.1

SCMS algorithm variable node information erase threshold"

Fig.2

M-SCMS algorithm variable node information erase threshold"

Fig.3

Percentage change in symbols for the algorithm"

Fig.4

Bit error rate of different algorithm with code length 2400"

Fig.5

Bit error rate of different algorithm with code length 3000"

Table 1

Computational complexity of different algorithm in a single iteration"

算 法MultiplicationDivisionAddiction
BP11NW-6（N+K）N（W+1）N（3W+1）
MS00N（4W-1）+K（lbW-2）
OMS00N（4W-1）+K（lbW-2）
SCMS00N（4W-1）+K（lbW-2）
DE-NMSNW0N（4W-1）+K（lbW-2）
LMMSE-MSNW0N（4W-1）+K（lbW-2）
M-SCMS2NW0N（5W-1）+K（lbW-2）

Fig.6

Average number of iterations of different decoding algorithm"

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