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Труды Московского физико-технического института

Optimization of the min-sum decoding algorithm for low-density parity-check codes

V. Sh. Le

Published Jan 1, 2021• 1 citation
Publisher
DOI
10.53815/20726759_2021_13_1_16

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Design of quantized Min-Sum decoder for low-density parity-check (LDPC) codes is very important for the application of LDPC codes.Based on the theory of density evolution,the paper proposes an algorithm to optimize the uniform or ununiform quantized Min-Sum decoder for regular or irregular LDPC codes.Compared with the continuous Sum-Product decoder,the proposed quantized decoder can reduce computational cost with little performance degradation, involving only integer addition calculations.

•Jan 1, 2005•0 citations
Low Density Parity Check (LDPC) code approaches Shannon–limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program, and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.
Abstract ― Low Density Parity Check (LDPC) code approaches Shannon–limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program, and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.