Equalizer for an IR-wireless LAN using RBF neural networks

The application of a RBF (radial basis function) neural network to an adaptive equalizer at the receiver of a wireless IR-LAN is considered. Fixing the decision threshold and classifying the received binary signals are the main functions of the RBF. The general problem of equalization binary signals...

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Bibliographic Details
Published in1993 18th Conference on Local Computer Networks pp. 461 - 466
Main Authors Perez-Jimenez, R., Martin-Bernardo, J., Melian, V.M., Ruiz-Alzola, J., Betancor, M.J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1993
Subjects
Adaptive equalizers
Computational complexity
Dispersion
Intersymbol interference
Local area networks
Multilayer perceptrons
Neural networks
Noise cancellation
Telecommunications
Working environment noise
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Summary:The application of a RBF (radial basis function) neural network to an adaptive equalizer at the receiver of a wireless IR-LAN is considered. Fixing the decision threshold and classifying the received binary signals are the main functions of the RBF. The general problem of equalization binary signals, passed through a dispersive channel and corrupted with noise, is briefly described. The characterization of the receiver and the effects of both Gaussian and shot noise over the signals are studied. A possible architecture for the equalizer and a comparison with other classical structures (multilayer perceptron and linear transversal equalizer), as well as simulation results are given. Considerations about the way of reducing computational complexity are proposed.
ISBN:0818645105
9780818645105
ISSN:0742-1303
DOI:10.1109/LCN.1993.591261