We investigate Cox processes of random point patterns in the Euclidean plane, which are located on the edges of random geometric graphs. Such Cox processes have applications in the performance analysis and strategic planning of both wireless and wired telecommunication networks. They simultaneously allow to represent the underlying infrastructure of the network together with the locations of network components. In particular, we analyze the Palm version X* of stationary Cox processes X living on random graphs that are built by the edges of an iterated random tessellation T. We derive a representation formula for the Palm version T* of T which includes the initial tessellation T0and the component tessellation T1of T as well as their Palm versions T*0and T*1. Using this formula, we are able to construct a simulation algorithm for X* if both T0, T1and their Palm versions T*0, T*1can be simulated. This algorithm for X* extends earlier results for Cox processes on simpler (non-iterated) tessellations. It can be used, for example, in order to estimate the probability densities of various connection distances, which are important performance characteristics of telecommunication networks. In a numerical study we consider the particular case that T0is a Poisson-Voronoi tessellation and T1is a Poisson line tessellation.

Stochastic models for hierarchical telecommunication networks are considered, which can be applied to the analysis and planning of large wireless networks. The network geometry is modelled by random geometric graphs, and the locations of network nodes by point processes on the edges of these graphs. In particular, the locations of high-level components (HLC) are modeled by Cox processes concentrated on the edge sets of random graphs, where their serving zones are the cells of Voronoi tessellations induced by these Cox processes. The locations of low-level components (LLC) are either modeled by planar Poisson processes or by Cox processes concentrated on the same edge sets as the HLC. Distributional properties of distances between the locations of network nodes are closely related with the interference geometry and, consequently, the performance of wireless networks. Representation formulas are derived for the distribution function and density of the typical Euclidean connection distance between LLC and HLC. They lead to suitable estimators of these characteristics, which can be computed by Monte Carlo simulation of the typical serving zone and the typical segment system in it, respectively.