A neural-based digital communication receiver for inter-symbol interference and white Gaussian noise channels

Author

Bang, Sa H. ; Sheu, Bing J.

Author_Institution

Univ. of Southern California, Los Angeles, CA, USA

Volume

6

fYear

1992

fDate

10-13 May 1992

Firstpage

2933

Abstract

A multilayer perceptron with the extended Kalman filter (EKF) training algorithm is investigated as a communication receiver when intersymbol interference and white Gaussian noise are present. Besides discussing the complexity of the EKF algorithm, it is shown that the EKF has better performance than the conventional backpropagation (BP) training algorithm in the sense that is requires less training steps and also results in proper training even when the BP did not. With 2500 training symbols, the EKF resulted in about 4-dB performance improvement over the conventional BP

Keywords

Kalman filters; digital communication systems; feedforward neural nets; intersymbol interference; white noise; extended Kalman filter; inter-symbol interference; multilayer perceptron; neural-based digital communication receiver; training algorithm; training symbols; white Gaussian noise channels; Digital communication; Equalizers; Fading; Finite impulse response filter; Gaussian noise; Interference; Maximum likelihood estimation; Multilayer perceptrons; Pulse modulation; Viterbi algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0593-0

Type

conf

DOI

10.1109/ISCAS.1992.230636

Filename

230636