A correlated shadowing model for urban wireless networks

François Baccelli 1, 2 Xinchen Zhang 1
2 DYOGENE - Dynamics of Geometric Networks
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : This paper presents an analytically tractable stochastic geometry model for urban wireless networks, where the locations of the nodes and the shadowing are highly correlated and different path loss functions can be applied to line-of-sight (LOS) and non-line-of-sight (NLOS) links. Using a distance-based LOS path loss model and a blockage (shadowing)-based NLOS path loss model, we are able to derive the distribution of the interference observed at a typical location and the joint distribution at different locations. When applied to cellular networks, this model leads to tractable expressions for the coverage probability (SINR distribution). We show that this model captures important features of urban wireless networks, which cannot be analyzed using existing models. The numerical results also suggest that even in the presence of significant penetration loss, ignoring the NLOS interference can lead to erroneous estimations on coverage. They also suggest that allowing users to be associated with NLOS BSs may provide a non-trivial gain on coverage.
Type de document :
Communication dans un congrès
Computer Communications (INFOCOM), 2015 IEEE Conference on, Apr 2015, Kowloon, China. pp.801-809, Computer Communications (INFOCOM), 2015 IEEE Conference on 〈10.1109/INFOCOM.2015.7218450〉
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https://hal.inria.fr/hal-01259166
Contributeur : Alain Monteil <>
Soumis le : mercredi 20 janvier 2016 - 09:03:58
Dernière modification le : mardi 24 avril 2018 - 17:20:14

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François Baccelli, Xinchen Zhang. A correlated shadowing model for urban wireless networks. Computer Communications (INFOCOM), 2015 IEEE Conference on, Apr 2015, Kowloon, China. pp.801-809, Computer Communications (INFOCOM), 2015 IEEE Conference on 〈10.1109/INFOCOM.2015.7218450〉. 〈hal-01259166〉

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