Theoretical Models for UMTS Radio Networks

Geerdes, Hans‐Florian; Eisenblätter, Andreas; Słobodzian, Piotr; Döhler, Mischa; Zdunek, Rafał; Gould, Peter; Nawrocki, M.J.

doi:10.1002/0470030569.ch6

Cited by 2 publications

(2 citation statements)

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“…Three widely different scenarios are used, in particular we illustrate the model for designing 3.5 GHz WiMAX networks over a six year period (N periods = 6) for three scenarios: 4 • Oslo (Norway)-an urban/suburban scenario covering 24 km 2 with 7,658 users and 29 potential sites. • Oxford (UK)-a high density urban scenario covering 11 km 2 with 5,381 users and 18 potential sites.…”

Section: Optimization Of Practical Scenariosmentioning

confidence: 99%

“…The use of automated planning and modelling in aiding the design of wireless communications networks has become widespread, though primarily in the area of mobile telephony, specifically GSM [1,2] and UMTS [3][4][5][6], but also for generic broadband fixed wireless access (BFWA) [7], WiMAX [8] and WLAN [9,10]. Although the main use of automated planning, including the use of mathematical programming techniques [2,11], has been in producing and configuring individual networks, they also have an important role to play in evaluating different economic and technical scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Modelling and planning fixed wireless networks

et al. 2008

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This paper describes a mathematical model for the automated design of fixed wireless access networks (FWA) through the automatic selection and configuration of base station sites. An optimisation algorithm is presented which generates the fixed wireless access network infrastructure design, and results are presented to illustrate the use of the model and its implementation. Economic measures based on the net present value (NPV) are defined to assess the financial viability of potential network designs. The NPV is used within the mathematical optimization framework to produce cost-effective deployments that maximize economic performance while maintaining technical constraints on the network. The model takes into account time-varying input parameters on CapEx, OpEx, revenues and subscriber requirements to model the dynamic nature of the market. Technical radio constraints taken into account include downlink area coverage, interference, capacity and availability. The model and optimisation framework are illustrated by considering the deployment and configuration of infrastructure for three scenarios representing urban, suburban and rural regions. Experiments illustrating the staged deployment of infrastructure over a number of time periods are also presented.

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Section: Optimization Of Practical Scenariosmentioning

confidence: 99%