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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Independent Component Analysis For Radio Network Prediction
Enhancement |
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Authors: |
Zakaria
Nouir, Berna Sayrac, Benoît Fourestié, Walid Tabbara Françoise Brouaye |
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Published in:
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(2006).ECMS
2006 Proceedings edited by: W. Borutzky, A. Orsoni, R. Zobel. European
Council for Modeling and Simulation. doi:10.7148/2006 ISBN:
0-9553018-0-7 20th
European Conference on Modelling and Simulation, Bonn,
May 28-31, 2006 |
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Citation
format: |
Nour, Z., Sayrac,
B., Fourestié, B., & Tabbara,
W. (2006). Independent Component Analysis For Radio Network Prediction
Enhancement. ECMS 2006 Proceedings edited by: W. Borutzky,
A. Orsoni, R. Zobel
(pp. 51-55). European Council for Modeling and Simulation. doi:10.7148/2006-0051 |
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DOI: |
http://dx.doi.org/10.7148/2006-0051 |
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Abstract: |
We propose a
method to enhance the quality and precision of prediction results using
measurements in the context of radio network modelling.
The proposed me- thod involves the use of an Independent
Component Analysis (ICA) block and a MultiLayer
Perceptron (MLP) Artificial Neural Network (ANN). The role of the ICA block
is to make the variables at the input of the ANN statistically independent so
that it can perform its learning and prediction on individual one-dimensional
distributions. The application of the proposed method to a third generation
cellular radio network prediction tool has shown that without ICA, ANN
training has a poor performance. We have also shown that, in the proposed
scheme, ICA performs better than Principle Component Analysis (PCA). This en- hancement method can advantageously be used
to calibrate prediction results according to measurements. |
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