Soft decision decoding of linear block codes using genetic algorithms

Harpal Maini, Kishan Mehrotra, Chilukuri Mohan, Sanjay Ranka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Soft decision decoding is a difficult search problem, for which optimal algorithms are computationally intractable. Genetic algorithms (GA) are stochastic optimisation techniques that have successfully solved many difficult search problems. We have developed a high performance GA for suboptimal soft decision decoding of binary linear block codes, which gives bit error probabilities as low as 0.00183 for a [104, 52] extended quadratic residue code with a signal-to-noise ratio of 2.5 dB, exploring only 30,000 codewords, whereas the search space contains 101/5 codewords. Success ensues from the use of a new crossover operator that exploits problem-specific knowledge.

Original languageEnglish (US)
Title of host publicationProceedings - 1994 IEEE International Symposium on Information Theory, ISIT 1994
Number of pages1
DOIs
StatePublished - Dec 1 1994
Event1994 IEEE International Symposium on Information Theory, ISIT 1994 - Trondheim, Norway
Duration: Jun 27 1994Jul 1 1994

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095

Other

Other1994 IEEE International Symposium on Information Theory, ISIT 1994
Country/TerritoryNorway
CityTrondheim
Period6/27/947/1/94

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Soft decision decoding of linear block codes using genetic algorithms'. Together they form a unique fingerprint.

Cite this