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AuthorVoß, Floriandc.contributor.author
Date of accession2016-03-15T06:23:08Zdc.date.accessioned
Available in OPARU since2016-03-15T06:23:08Zdc.date.available
Year of creation2009dc.date.created
AbstractThe present thesis is motivated by a joint research project of the Institute of Stochastics at Ulm University and Orange Labs in Issy les Moulineaux, Paris, which deals with the analysis of telecommunication networks. During the last years the Stochastic Subscriber Line Model (SSLM) has been developed in this cooperation and it is still extended. The SSLM is a flexible model for telecommunication networks, especially designed for access networks in urban areas. It utilizes tools from Stochastic Geometry in order to represent the different parts of the network by spatial stochastic models which only depend on a small number of parameters. The aim of this thesis is to analyze so-called typical connection lengths in the SSLM in various ways. One part of the thesis focuses on the estimation of their densities and distribution functions via Monte-Carlo simulation. The developed estimators are all based on samples of the so-called typical serving zone. Therefore, simulation algorithms for the typical serving zone are derived for various network models. Another part of the thesis deals with limit theorems for the typical connection length. In particular, it is shown that the distribution of the typical connection length converges to well-known distributions if the parameters of the underlying network model tend to extremal cases. Both results, the estimated distributions and the asymptotic distributions, are used in order to obtain approximative parametric densities for the typical connection length. These parametric densities are finally compared to empirical distributions computed from huge databases without using any spatial information. It is shown that the obtained parametric densities fit quite well to real network data. In the final part of the thesis, required capacities at different locations of the network are analyzed.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandard (Fassung vom 01.10.2008)dc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v2dc.rights.uri
KeywordMarked point processdc.subject
KeywordPalm distributionsdc.subject
KeywordRandom tessellationdc.subject
KeywordScaling limitsdc.subject
Dewey Decimal GroupDDC 510 / Mathematicsdc.subject.ddc
LCSHStochastic modelsdc.subject.lcsh
LCSHTelecommunication networksdc.subject.lcsh
TitleSpatial stochastic network models - scaling limits and Monte-Carlo methodsdc.title
Resource typeDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-1786dc.identifier.doi
PPN621072133dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-72245dc.identifier.urn
GNDMonte-Carlo-Simulationdc.subject.gnd
FacultyFakultät für Mathematik und Wirtschaftswissenschaftenuulm.affiliationGeneral
Date of activation2010-02-24T15:18:35Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
Shelfmark print versionZ: J-H 13.587; N: J-H 9.872uulm.shelfmark
DCMI TypeTextuulm.typeDCMI
VTS ID7224uulm.vtsID
CategoryPublikationenuulm.category
Bibliographyuulmuulm.bibliographie


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