A Simulation study on Interference in CSMA/CA Ad-Hoc Networks using Point Process

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Master's thesis
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Date

2010

Department

Sähkötekniikan laitos

Major/Subject

Tietoliikennetekniikka

Mcode

S-72

Degree programme

Language

en

Pages

[11] + 79

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Abstract

Performance of wireless ad-hoc networks is essentially degraded by co-channel interference. Since the interference at a receiver crucially depends on the distribution of the interfering transmitters, mathematical technique is needed to specifically model the network geometry where a number of nodes are randomly spread. This is why stochastic geometry approach is required. In this thesis, we study about stochastic point processes such as Poisson Point Process, Matérn Point Process, and Simple Sequential Inhibition Point Process. The interference distributions resulting from the different point process are compared, and in CSMA/CA networks, point process's limitation issue such as the under-estimation of the node density is discussed. Moreover, we show that the estimated interference distribution obtained by Network Simulator 2, is different with respect to the different point process. Even if there is the existence of gap between the distributions from the point processes and the simulator due to active factors, they all offer similar shape which follows a peak and an asymmetry with a more or less heavy tail. This observation has promoted an interest in characterizing the distribution of the aggregated interference with the Log-normal, Alpha-stable, and Weibull distributions as a family of heavy tail distributions. Even though hypothesis tests have mostly led to the reject of the null assumption, that the interference distribution by the simulator, is a random sample from these heavy tailed distributions, except for the Alpha-stable distribution in high density. The hypothesis statistics systematically yield agreement on the choice of the better approximation. Moreover, the log probability process certainly makes it possible to reliably select the most similar heavy tailed distribution to the empirical data set on the variation of node density.

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Supervisor

Jäntti, Riku

Thesis advisor

Hwang, June

Keywords

interference modelling, stochastic geometry, poisson point process, matérn point process, simple sequential inhibition process

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https://urn.fi/URN:NBN:fi:aalto-201203151585

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[dipl] Sähkötekniikan korkeakoulu / ELEC