A new genetics-aided message passing decoding algorithm for LDPC codes

Jui Hui Hung*, Yi De Lu, Sau-Gee Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The popular LDPC decoding algorithms based on the message passing (MP) algorithm have high decoding performances. However, they are noticeably inferior to the maximum likelihood (ML) decoding algorithm. This work proposes a genetics-aided message passing (GA-MP) algorithm by applying a new genetic algorithm to MP algorithm. As a result, significantly performance improvement over MP algorithm can be achieved. Besides, compared with other genetic-aided decoding algorithms, the proposed algorithm has much better performances and much lower computational complexity. Simulations show that the de-coding performance of GA-MP algorithm can achieve perfor-mances very close to the algorithm, while outperform MP algo-rithm. Besides, its performance will grow proportionally with the generation number without leveling off as observed in conven-tional MP algorithms, under high SNR condition.

Original languageEnglish
Title of host publication2012 IEEE Vehicular Technology Conference, VTC Fall 2012 - Proceedings
DOIs
StatePublished - 1 Dec 2012
Event76th IEEE Vehicular Technology Conference, VTC Fall 2012 - Quebec City, QC, Canada
Duration: 3 Sep 20126 Sep 2012

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252

Conference

Conference76th IEEE Vehicular Technology Conference, VTC Fall 2012
CountryCanada
CityQuebec City, QC
Period3/09/126/09/12

Keywords

  • Channel coding
  • Genetic algorithm
  • LDPC codes
  • ML decoding

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