Convergence of Min-Sum Decoding of LDPC codes under a Gaussian Approximation 


Vol. 28,  No. 10, pp. 936-941, Oct.  2003


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  Abstract

Density evolution was developed as a method for computing the capacity of low-density parity-check(LDPC) codes under the sum-product algorithm [1]. Based on the assumption that the passed messages on the belief propagation model can be approximated well by Gaussian random variables, a modified and simplified version of density evolution technique was introduced in [2]. Recently, the min-sum algorithm was applied to the density evolution of LDPC codes as an alternative decoding algorithm in [3]. Next question is how the min-sum algorithm is combined with a Gaussian approximation. In this paper, the capacity of various rate LDPC codes is obtained using the min-sum algorithm combined with the Gaussian approximation, which gives a simplest way of LDPC code analysis. Unlike the sum-product algorithm, the symmetry condition [4] is not maintained in the min-sum algorithm. Therefore, the variance as well as the mean of Gaussian distribution are recursively computed in this analysis. It is also shown that the. min-sum threshold under a gaussian approximation is well matched to the simulation results.

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  Cite this article

[IEEE Style]

J. Heo, "Convergence of Min-Sum Decoding of LDPC codes under a Gaussian Approximation," The Journal of Korean Institute of Communications and Information Sciences, vol. 28, no. 10, pp. 936-941, 2003. DOI: .

[ACM Style]

Jun Heo. 2003. Convergence of Min-Sum Decoding of LDPC codes under a Gaussian Approximation. The Journal of Korean Institute of Communications and Information Sciences, 28, 10, (2003), 936-941. DOI: .

[KICS Style]

Jun Heo, "Convergence of Min-Sum Decoding of LDPC codes under a Gaussian Approximation," The Journal of Korean Institute of Communications and Information Sciences, vol. 28, no. 10, pp. 936-941, 10. 2003.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880