A comparative study on the channel modeling using feedforward and recurrent neural network structures

In digital mobile communication, the non-stationary channel linear modeling become insufficient for channel nonlinear variations. The objective of this work is to select a suitable neural structure for the channel modeling. We present the advantages of a new neural structure, which is the modified E...

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Published inConference proceedings - IEEE International Conference on Systems, Man, and Cybernetics Vol. 4; pp. 3759 - 3763 vol.4
Main Authors Chagra, W., Abdennour, R.B., Bouani, F., Ksouri, M., Favier, G.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 1998
Subjects
Automatic control
Computational efficiency
Digital communication
Educational institutions
Finite impulse response filter
Multilayer perceptrons
Neural networks
Nonlinear distortion
Recurrent neural networks
Signal processing algorithms
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Summary:In digital mobile communication, the non-stationary channel linear modeling become insufficient for channel nonlinear variations. The objective of this work is to select a suitable neural structure for the channel modeling. We present the advantages of a new neural structure, which is the modified Elman network (MEN), applied to digital communication problems such us the channel modeling. By comparison with the multilayer perceptron (MLP), we deduce that the MEN structure has proved the same results with MLP but involve much less computational cost.
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ISBN:9780780347786
0780347781
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.1998.726672