Stochastic geometry modeling and analysis of single- and multi-cluster wireless networks
Journal article, 2018

This paper develops a stochastic geometry-based approach for the modeling and analysis of single- and 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-selection, where the receiver is served by the closest transmitter among all transmitters, and uniform-selection, where the serving transmitter is selected randomly with uniform distribution. Second, using Matern cluster processes, we extend our model and analysis to multi-cluster wireless networks. Here, two types of receivers are modeled, namely, closed- and open-access receivers. Closed-access receivers are distributed around the cluster centers of the transmitters according to a symmetric normal distribution and can be served only by the transmitters of their corresponding clusters. Open-access receivers, on the other hand, are placed independently of the transmitters and can be served by all transmitters. In all cases, the link distance distribution and the Laplace transform (LT) of the interference are derived. We also derive closed-form lower bounds on the LT of the interference for single-cluster wireless networks. The impact of different parameters on the performance is also investigated.

Poisson point process

clustered wireless networks

Matern cluster process

Stochastic geometry


Seyed Mohammad Azimi-Abarghouyi

Sharif University of Technology

Behrooz Makki

Ericsson AB

Martin Haenggi

University of Notre Dame

Masoumeh Nasiri-Kenari

Sharif University of Technology

Tommy Svensson

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

IEEE Transactions on Communications

00906778 (ISSN)

Vol. 66 10 4981-4996 8368129

Subject Categories


Communication Systems

Probability Theory and Statistics



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