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Stochastic models for cellular networks planning and performance assessment

Abstract : With the booming of the ubiquitous and nomad Internet of Things, wireless systems and networks must support the limitless development of a digitalized eco-system. Being the backbone of the connected devices, asserting and optimizing wireless network performance is of a major importance. This dissertation aims at introducing methods and numerical analysis frameworks to enhance our comprehension of performance at a network level. Indeed, network performance has been widely explored from the point of view of the link capacity. Thanks to stochastic geometry and point process, we are able characterize the influence of the positions of the antennas. In this dissertation, the \beta-Ginibre point process is chosen to model the locations of the base stations in the plain. The \beta-Ginibre is a repulsive point process in which repulsion is controlled by the parameter. When \beta tends to zero, the point process converges in law towards a Poisson point process. If \beta equals to one it becomes a Ginibre point process. Simulations on real data collected in France show that base station locations can be fitted with a \beta-Ginibre point process. Moreover we prove that their superposition tends to a Poisson point process as it can be seen from real data. Qualitative interpretations on deployment strategies are derived from the model fitting of the raw data. The parameter that represents the deployment strategy of a operator, is also an indicator of the overall signal quality in the network : the more regular the deployment is, the better the overall signal quality. In order to quantify the gain in performance induced by a higher , a interference limited network model based on marked point process and loss probability has been introduced. In order to generalize performance analysis to any networks, a scheme based on the Cournot-Nash equilibria is investigated. Under this general framework, only the signal quality between nodes is required to derive a resource allocation strategy for the overall network. Supply in resources of the network and traffic requirements are modeled by probability measures. The optimal resource allocation strategy is derived by the coupling between the two probability measures that minimizes a specific quadratic objective function. Numerical analysis highlights that there exists an optimal working point, where users satisfaction and network occupancy are equal.
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Submitted on : Sunday, December 9, 2018 - 10:55:22 PM
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Jean-Sébastien Gomez. Stochastic models for cellular networks planning and performance assessment. Computer Science [cs]. EDITE, 2018. English. ⟨tel-01949293⟩

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