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Studying the SINR process of the typical user in Poisson networks by using its factorial moment measures

Bartlomiej Blaszczyszyn 1 Holger Keeler 1 
1 DYOGENE - Dynamics of Geometric Networks
DI-ENS - Département d'informatique - ENS Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : Based on a stationary Poisson point process, a wireless network model with random propagation effects (shadowing and/or fading) is considered in order to examine the process formed by the signal-to-interference-plus-noise ratio (SINR) values experienced by a typical user with respect to all base stations in the down-link channel. This SINR process is completely characterized by deriving its factorial moment measures, which involve numerically tractable, explicit integral expressions. This novel framework naturally leads to expressions for the k-coverage probability, including the case of random SINR threshold values considered in multi-tier network models. While the k-coverage probabilities correspond to the marginal distributions of the order statistics of the SINR process, a more general relation is presented connecting the factorial moment measures of the SINR process to the joint densities of these order statistics. This gives a way for calculating exact values of the coverage probabilities arising in a general scenario of signal combination and interference cancellation between base stations. The presented framework consisting of mathematical representations of SINR characteristics with respect to the factorial moment measures holds for the whole domain of SINR and is amenable to considerable model extension.
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Contributor : Bartlomiej Blaszczyszyn Connect in order to contact the contributor
Submitted on : Wednesday, March 11, 2015 - 1:02:13 PM
Last modification on : Thursday, March 17, 2022 - 10:08:44 AM
Long-term archiving on: : Monday, April 17, 2017 - 6:28:52 AM


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Bartlomiej Blaszczyszyn, Holger Keeler. Studying the SINR process of the typical user in Poisson networks by using its factorial moment measures. IEEE Transactions on Information Theory, 2015, 61 (12), pp.6774 - 6794. ⟨10.1109/TIT.2015.2484348⟩. ⟨hal-00932106v3⟩



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