Application of recurrent neural networks to communication channel equalization

Author

Bradley, M.J. ; Mars, P.

Author_Institution

Sch. of Eng., Durham Univ., UK

Volume

5

fYear

1995

fDate

9-12 May 1995

Firstpage

3399

Abstract

The paper examines the mechanism by which recurrent neural networks (RNNs) achieve equalization whilst operating on simple digital communication channels. The mode of operation is seen to be essentially similar to the conventional decision feedback equalizer (DFE) and the RNN node nonlinearity is identified as a limiting factor. Two versions of an alternative RNN structure are formulated for channels with longer impulse responses based on soft-decision feedback. Simulations demonstrate the improved BER performance compared with the DFE

Keywords

digital communication; equalisers; error statistics; recurrent neural nets; telecommunication channels; transient response; BER performance; RNN node nonlinearity; communication channel equalization; decision feedback equalizer; digital communication channels; impulse responses; recurrent neural networks; soft-decision feedback; Bit error rate; Communication channels; Decision feedback equalizers; Delay lines; Digital communication; Intersymbol interference; Neural networks; Neurofeedback; Pattern classification; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on

Conference_Location

Detroit, MI

ISSN

1520-6149

Print_ISBN

0-7803-2431-5

Type

conf

DOI

10.1109/ICASSP.1995.479715

Filename

479715