Modeling of Dense CSMA Networks using Random Sequential Adsorption Process

TL;DR

This paper introduces a novel modeling approach for dense CSMA wireless networks using the RSA process, providing accurate analytical tools for medium access probability and interference estimation, validated by simulations.

Contribution

It applies the RSA process from physics to model AP locations in CSMA networks, offering improved accuracy over traditional models and deriving key network performance metrics.

Findings

RSA-based model outperforms MHPP-II in density accuracy

Analytical expressions for access probability and coverage probability

Validation through extensive Monte Carlo simulations

Abstract

We model a dense wireless local area network where the access points (APs) employ carrier sense multiple access (CSMA)-type medium access control protocol. In our model, the spatial locations of the set of active APs are modeled using the random sequential adsorption (RSA) process, which is more accurate in terms of the density of active APs compared to the Mat\'ern hard-core point process of type-II (MHPP-II) commonly used for modeling CSMA networks. Leveraging the theory of the RSA process from the statistical physics literature, we provide an approximate but accurate analytical result for the medium access probability of the typical AP in the network. Further, we present a numerical approach to determine the pair correlation function $(\mathtt{PCF})$, which is useful for the accurate estimation of the interference statistics. Using the $\mathtt{PCF}$ result, we derive the signal-to-interference-plus-noise ratio coverage probability of the typical link in the network. We validate the accuracy of the theoretical results through extensive Monte Carlo simulations.