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dc.contributor.authorAmar, Eya Ben
dc.contributor.authorRached, Nadhir Ben
dc.contributor.authorHaji-Ali, Abdul-Lateef
dc.contributor.authorTempone, Raul
dc.date.accessioned2022-01-12T12:28:36Z
dc.date.available2022-01-12T12:28:36Z
dc.date.issued2022-01-04
dc.identifier.urihttp://hdl.handle.net/10754/674927
dc.description.abstractWhen assessing the performance of wireless communication systems operating over fading channels, one often encounters the problem of computing expectations of some functional of sums of independent random variables (RVs). The outage probability (OP) at the output of Equal Gain Combining (EGC) and Maximum Ratio Combining (MRC) receivers is among the most important performance metrics that falls within this framework. In general, closed form expressions of expectations of functionals applied to sums of RVs are out of reach. A naive Monte Carlo (MC) simulation is of course an alternative approach. However, this method requires a large number of samples for rare event problems (small OP values for instance). Therefore, it is of paramount importance to use variance reduction techniques to develop fast and efficient estimation methods. In this work, we use importance sampling (IS), being known for its efficiency in requiring less computations for achieving the same accuracy requirement. In this line, we propose a state-dependent IS scheme based on a stochastic optimal control (SOC) formulation to calculate rare events quantities that could be written in a form of an expectation of some functional of sums of independent RVs. Our proposed algorithm is generic and can be applicable without any restriction on the univariate distributions of the different fading envelops/gains or on the functional that is applied to the sum. We apply our approach to the Log-Normal distribution to compute the OP at the output of diversity receivers with and without co-channel interference. For each case, we show numerically that the proposed state-dependent IS algorithm compares favorably to most of the well-known estimators dealing with similar problems.
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2019-CRG8-4033 and the Alexander von Humboldt Foundation
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2201.01340.pdf
dc.rightsArchived with thanks to arXiv
dc.subjectOutage probability
dc.subjectMonte Carlo
dc.subjectrare event
dc.subjectimportance sampling
dc.subjectstochastic optimal control
dc.titleEfficient Importance Sampling Algorithm Applied to the Performance Analysis of Wireless Communication Systems Estimation
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institution2Chair of Mathematics for Uncertainty Quantification, Department of Mathematics, RWTH Aachen University, Aachen, Germany
dc.contributor.institutionSchool of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK
dc.contributor.institutionAlexander von Humboldt Professor in Mathematics for Uncertainty Quantification, RWTH Aachen University, Aachen, Germany.
dc.identifier.arxivid2201.01340
kaust.personAmar, Eya Ben
kaust.personTempone, Raul
kaust.grant.numberOSR-2019-CRG8-4033
refterms.dateFOA2022-01-12T12:29:36Z
kaust.acknowledged.supportUnitCRG
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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