Title :
Recurrent radial basis function networks for optimal blind equalization
Author :
Sueiro, Jesús Cid ; Figueiras-Vidal, Aníbal R.
Author_Institution :
ETSI Telecommun., Valladolid, Spain
Abstract :
A recurrent version of a radial basis function (RBF) network can compute optimal symbol-by-symbol decisions for equalizing Gaussian channels in digital communication systems, but the (linear or not) channel response and the noise variance must be known. Starting from theoretical considerations, a novel technique for learning the channel parameters in a non-supervised, non-decision directed way is proposed. This technique provides a simple and fast algorithm that can be used for tracking in time variant environments or for blind equalization purposes
Keywords :
Gaussian channels; digital communication; equalisers; feedforward neural nets; recurrent neural nets; telecommunication computing; Gaussian channels; channel parameters; channel response; digital communication systems; noise variance; optimal blind equalization; recurrent radial basis function networks; symbol-by-symbol decisions; Blind equalizers; Computer networks; Detectors; Digital communication; Gaussian noise; Radial basis function networks; Telecommunication standards; Vectors; Viterbi algorithm; Working environment noise;
Conference_Titel :
Neural Networks for Processing [1993] III. Proceedings of the 1993 IEEE-SP Workshop
Conference_Location :
Linthicum Heights, MD
Print_ISBN :
0-7803-0928-6
DOI :
10.1109/NNSP.1993.471831