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

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.

MIN-SUM 복호화 알고리즘을 이용한 LDPC 오류정정부호의 성능분석

최근에 소개된 density evolution 기법은 sum-product 알고리즘에서 LDPC 부호가 갖는 성능의 한계를 분석하였다[1]. 또한. Iterative decoding 알고리즘에서 전달되는 정보가 Gaussian 확률분포를 갖는 점을 이용하여 기존의 density evolution 기법을 단순화 시킨 연구결과가 소개되었다[2]. 한편. LDPC 부호의 한계 성능을 sum-product가 아닌 min-sum 알고리즘에서 분석한 결과가 최근에 발표되었다[3]. 본 논문에서는 이러한 일련의 연구 결과를 바탕으로 min-sum 알고리즘을 이용하면서 Gaussian 확률 분포 특성을 이용한 density evolution 기법을 소개한다. 제안된 density evolution 기법은 기존의 방법보다 적은 계산으로 정확한 threshold를 구할 수 있으며. 그 결과가 numerical simulation 결과와 잘 일치함을 나타내었다.