Fitting of stochastic telecommunication network models via distance measures and Monte–Carlo tests

We explore real telecommunication data describing the spatial geometrical structure of an urban region and we propose a model fitting procedure, where a given choice of different non-iterated and iterated tessellation models is considered and fitted to real data. This model fitting procedure is base...

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Bibliographic Details
Published in:Telecommunication systems 2006-04, Vol.31 (4), p.353-377
Main Authors: Gloaguen, C, Fleischer, F, Schmidt, H, Schmidt, V
Format: Article
Language:English
Subjects:
Algorithms
Communications networks
Computer simulation
Design analysis
Electric communication systems
Geometry
Infrastructure
Local loop
Mathematical models
Monte Carlo methods
Monte Carlo simulation
Roads & highways
Stochastic models
Strategic planning
Studies
Telecommunications
Telecommunications systems
Tessellation
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Summary:We explore real telecommunication data describing the spatial geometrical structure of an urban region and we propose a model fitting procedure, where a given choice of different non-iterated and iterated tessellation models is considered and fitted to real data. This model fitting procedure is based on a comparison of distances between characteristics of sample data sets and characteristics of different tessellation models by utilizing a chosen metric. Examples of such characteristics are the mean length of the edge-set or the mean number of vertices per unit area. In particular, after a short review of a stochastic-geometric telecommunication model and a detailed description of the model fitting algorithm, we verify the algorithm by using simulated test data and subsequently apply the procedure to infrastructure data of Paris.
ISSN:1018-4864
1572-9451
DOI:10.1007/s11235-006-6723-3