DocumentCode
1682932
Title
Adaptive channel equalization using EKF-CRTRL neural networks
Author
Henrique, Pedro ; Coelho, Gouvsa
Author_Institution
Electron. & Telecommun. Dept., State Univ. of Rio de Janeiro, Brazil
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1195
Lastpage
1199
Abstract
The purpose of this paper is to apply the complex real time recurrent learning-fully recurrent neural network extended Kalman filter (CRTRL-EKF), trained in an adaptive equalization for cellular communications channels. Numerical results are presented to illustrate the method using the wide sense stationary-uncorrelated scattering channel model
Keywords
Kalman filters; adaptive equalisers; cellular radio; learning (artificial intelligence); real-time systems; recurrent neural nets; telecommunication channels; time division multiple access; Kalman filter; TDMA cellular systems; adaptive channel equalization; cellular communications channels; real time learning; recurrent neural network; uncorrelated scattering channel model; wide sense stationary channel model; Adaptive equalizers; Cellular networks; Cellular neural networks; Communication channels; Equations; Feedback loop; Neural networks; Neurons; Recurrent neural networks; State-space methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007664
Filename
1007664
Link To Document