Modeling dense urban wireless networks with 3D stochastic geometry

Modeling Dense Urban Networks with 3D Stochastic Geometry

Over the past decade, many works on the modeling of wireless networks using stochastic geometry have been proposed. Results about probability of coverage, throughput or mean interference, have been provided for a wide variety of networks (cellular, ad-hoc, cognitive, sensors, etc). These results notably allow to tune network protocol parameters. Nevertheless, in their vast majority, these works...

Stochastic Geometry for Wireless Networks

Covering point process theory, random geometric graphs, and coverage processes, this rigorous introduction to stochastic geometry will enable you to obtain powerful, general estimates and bounds of wireless network performance, and make good design choices for future wireless architectures and protocols that efficiently manage interference effects. Practical engineering applications are integra...

Stochastic Geometry and Wireless Networks; Vol.II: Applications

Stochastic Geometry Modeling and Analysis of Single- and Multi-Cluster Wireless Networks

This paper develops a stochastic geometry-based approach for the modeling and analysis of singleand multi-cluster wireless networks. We first define finite homogeneous Poisson point processes to model the number and locations of the transmitters in a confined region as a single-cluster wireless network. We study the coverage probability for a reference receiver for two strategies; closest-selec...

Stochastic geometry modeling of spatial wireless communication networks — beyond the Poisson assumption

There is a need for simple, yet realistic models for communication network architectures capturing the spatial distribution of the elements of the network and having a limited number of parameters. This can be obtained via probabilistic modeling based on point processes and stochastic geometry. The probabilistic setting reflects the network variability in time and space, this is particularly re...