NDLI: Measurement Aided 3G Radio Network Prediction: Fuzzy Bayesian Framework

Content Provider	IEEE Xplore Digital Library
Author	Nouir, Z. Sayrac, B. Fourestie, B. Tabbara, W. Brouaye, F.
Copyright Year	2007
Description	Author affiliation: France Telecom Res. & Dev. Div. (Nouir, Z.; Sayrac, B.; Fourestie, B.)
Abstract	We have used measurements taken on real network to enhance the performance of our radio network planning tool. A distribution learning technique is adopted to realize this challenged task. To ensure better generalization capabilities of the learning algorithm, a preprocessing of data is required and involves the use of a clustering algorithm that divides the whole learning space into subspaces. In this paper we apply a new fuzzy clustering algorithm to a prediction tool of a third generation (3G) cellular radio network. Results show that the differences observed between simulations and measurements can be considerably diminished and the generalization capacity is enhanced thanks to the proposed clustering algorithm. This algorithm performs well than classical c-means algorithm. We can then predict with enhanced accuracy new configuration for which we don't have measurements, as long they are not very different from learned configurations.
Starting Page	715
Ending Page	719
File Size	415004
Page Count	5
File Format	PDF
ISBN	1424402662
ISSN	15502252
DOI	10.1109/VETECS.2007.157
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-22
Publisher Place	Ireland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radio network Bayesian methods Clustering algorithms Telecommunication traffic Traffic control Artificial neural networks Electronic mail Land mobile radio cellular systems Computer networks Predictive models
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering Computer Science Applications

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