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

Author

Sazli, Murat H. ; Isik, Can

Author_Institution

Dept. of Electron. Eng., Ankara Univ.

fYear

2005

fDate

21-21 Dec. 2005

Firstpage

832

Lastpage

837

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

Keywords

AWGN channels; decoding; feedforward neural nets; matrix algebra; maximum likelihood estimation; turbo codes; AWGN channel; BCJR algorithm; decoding; feedforward neural network; matrix algebra; neural network implementation; turbo codes; Artificial neural networks; Convolutional codes; Feedforward neural networks; Image coding; Iterative algorithms; Iterative decoding; Matrices; Neural networks; Signal processing algorithms; Turbo codes;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Information Technology, 2005. Proceedings of the Fifth IEEE International Symposium on

Conference_Location

Athens

Print_ISBN

0-7803-9313-9

Type

conf

DOI

10.1109/ISSPIT.2005.1577207

Filename

1577207

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3038329