Analysis of a Proportionally Fair and Locally Adaptive spatial Aloha in Poisson Networks

François Baccelli 1, 2, 3 Bartlomiej Blaszczyszyn 2 Chandramani Singh 2
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 : The proportionally fair sharing of the capacity of a Poisson network using Spatial-Aloha leads to closed-form performance expressions in two extreme cases: (1) the case without topology information, where the analysis boils down to a parametric optimization problem leveraging stochastic geometry; (2) the case with full network topology information, which was recently solved using shot-noise techniques. We show that there exists a continuum of adaptive controls between these two extremes, based on local stopping sets, which can also be analyzed in closed form. We also show that these control schemes are implementable, in contrast to the full information case which is not. As local information increases, the performance levels of these schemes are shown to get arbitrarily close to those of the full information scheme. The analytical results are combined with discrete event simulation to provide a detailed evaluation of the performance of this class of medium access controls.
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Communication dans un congrès
INFOCOM, Apr 2014, Toronto, Canada. IEEE, 2014, Proc. of INFOCOM 2014. 〈10.1109/INFOCOM.2014.6848201〉
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François Baccelli, Bartlomiej Blaszczyszyn, Chandramani Singh. Analysis of a Proportionally Fair and Locally Adaptive spatial Aloha in Poisson Networks. INFOCOM, Apr 2014, Toronto, Canada. IEEE, 2014, Proc. of INFOCOM 2014. 〈10.1109/INFOCOM.2014.6848201〉. 〈hal-00849752〉

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