Performance and convergence analysis of tree-LDPC codes on the min-sum iterative decoding algorithm

Kwangseok Noh, Jun Heo

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, the performance of Tree-LDPC code 1 is presented based on the min-sum algorithm with scaling and the asymptotic performance in the water fall region is shown by density evolution. We presents that the Tree-LDPC code show a significant performance gain by scaling with the optimal scaling factor 3 which is obtained by density evolution methods. We also show that the performance of min-sum with scaling is as good as the performance of sum-product while the decoding complexity of min-sum algorithm is much lower than that of sum-product algorithm.

Original languageEnglish
Pages (from-to)1749-1750
Number of pages2
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE89-A
Issue number6
DOIs
Publication statusPublished - 2006 Jun 1
Externally publishedYes

Fingerprint

Iterative Decoding
Iterative decoding
LDPC Codes
Convergence Analysis
Performance Analysis
Scaling
Decoding
Optimal Scaling
Scaling Factor
Water

Keywords

  • Density evolution
  • Implementation
  • Iterative decoding
  • Scaling factor
  • Tree-LDPC codes

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems
  • Electrical and Electronic Engineering

Cite this

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AB - In this paper, the performance of Tree-LDPC code 1 is presented based on the min-sum algorithm with scaling and the asymptotic performance in the water fall region is shown by density evolution. We presents that the Tree-LDPC code show a significant performance gain by scaling with the optimal scaling factor 3 which is obtained by density evolution methods. We also show that the performance of min-sum with scaling is as good as the performance of sum-product while the decoding complexity of min-sum algorithm is much lower than that of sum-product algorithm.

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