Stochastic Geometry Modelling and Performance Evaluation of Cellular Networks

• Author / Creator

  Chen, Chunlin

• Stochastic geometry provides a way of defining and computing macroscopic properties of large scale wireless networks, by averaging over all possible spatial patterns of the network nodes. It abstracts the network as realizations of point process models, and analyzes the network performance in a probabilistic way. While stochastic geometry has its strength in theoretical analysis, statistical questions are seldom discussed and measurement-based validation of certain stochastic assumptions used in the literature is often not given. The aim in most of the related research work in the literature is to show what results can be obtained with stochastic geometry when assumptions of certain point process models are made, without necessarily fully justifying those assumptions. It is critical to find an accurate point process model that best reflects the spatial distribution of the network nodes before any attempts on the theoretical analysis of the underlying point process model. In addition, extensions of the analytical methodology
  used in Poisson models to more general point process models are often hindered due to the lack of closed-form empty space function and the probability generating functional (PGFL). In view of these problems, the thesis presents and describes a practical technique of statistical validation by fitting stationary and nonstationary point process models to real-life cellular networks using maximum likelihood/pseudolikelihood and
  minimum contrast methods. We also have studied the distributional properties of the empty space distances in the Matérn hard core point process of Type II, and proposed a
  piecewise probability density function for the empty space distance, including an exact expression and a heuristic formula, which can be fitted by aWeibull-like function. Furthermore,
  we have examined the properties of the PGFL for Poisson cluster processes, and studied the downlink coverage performance for a two-tier cellular network.

• Subjects / Keywords

  • Stochastic geometry
  • Stationary and nonsationary point processes
  • Matérn hard core point processes
  • Poisson cluser processes

• Graduation date

  Fall 2018

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3ZW19793

• License

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