Theoretical interpretation and investigation of a 2/n rate convolutional decoder based on recurrent neural networks

Author

Berber, Stevan M. ; Liu, Yi-Chun

Author_Institution

Dept. of Electr. & Electron. Eng., Auckland Univ., New Zealand

Volume

2

fYear

2003

fDate

15-18 Dec. 2003

Firstpage

1201

Abstract

In this paper a mathematical model of a 2/n rate conventional convolutional encoder/decoder system was developed to be applied for decoding using neural networks based on the gradient descent algorithm. The general expression for the energy function, needed for the recurrent neural networks decoding, is derived. Then, the expressions for the gradient decent updating rule are derived and the neural network decoder was designed. The coding scheme of a 2/3 rate code is simulated and results are compared with the results achieved in literature for one-input encoders.

Keywords

block codes; convolutional codes; decoding; digital communication; error statistics; gradient methods; recurrent neural nets; turbo codes; 2/n rate convolutional encoder; gradient descent algorithm; noise energy function; recurrent neural networks decoding; AWGN; Additive white noise; Artificial neural networks; Convolutional codes; Gaussian noise; Maximum likelihood decoding; Neural networks; Power engineering and energy; Recurrent neural networks; Viterbi algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on

Print_ISBN

0-7803-8185-8

Type

conf

DOI

10.1109/ICICS.2003.1292651

Filename

1292651