Neural network implementation of the BCJR algorithm based on reformulation using matrix algebra

Murat H. Sazli, Can Isik

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

2 Scopus citations

Abstract

In this paper, we show that the BCJR algorithm (or Bahl algorithm) can be implemented as a feedforward neural network structure based on a reformulation of the algorithm using matrix algebra. We verified through computer simulations that this novel neural network implementation yields identical results with the BCJR algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology
Pages832-837
Number of pages6
Volume2005
DOIs
StatePublished - 2005
Event5th IEEE International Symposium on Signal Processing and Information Technology - Athens, Greece
Duration: Dec 18 2005Dec 21 2005

Other

Other5th IEEE International Symposium on Signal Processing and Information Technology
CountryGreece
CityAthens
Period12/18/0512/21/05

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Keywords

  • Bahl algorithm
  • BCJR algorithm
  • MAP algorithm
  • Neural networks
  • Turbo codes
  • Turbo coding/decoding

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Sazli, M. H., & Isik, C. (2005). Neural network implementation of the BCJR algorithm based on reformulation using matrix algebra. In Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology (Vol. 2005, pp. 832-837). [1577207] https://doi.org/10.1109/ISSPIT.2005.1577207