Importance Sampling Estimator of Outage Probability under Generalized Selection Combining Model

Nadhir Ben Rached, Zdravko Botev, Abla Kammoun, Mohamed-Slim Alouini, Raul Tempone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We consider the problem of evaluating outage probability (OP) values of generalized selection combining diversity receivers over fading channels. This is equivalent to computing the cumulative distribution function (CDF) of the sum of order statistics. Generally, closed-form expressions of the CDF of order statistics are unavailable for many practical distributions. Moreover, the naive Monte Carlo method requires a substantial computational effort when the probability of interest is sufficiently small. In the region of small OP values, we propose instead an efficient, yet universal, importance sampling (IS) estimator that yields a reliable estimate of the CDF with small computing cost. The main feature of the proposed IS estimator is that it has bounded relative error under a certain assumption that is shown to hold for most of the challenging distributions. Moreover, an improvement of this estimator is proposed for the Pareto and the Weibull cases. Finally, the efficiency of the proposed estimators are investigated through various numerical experiments.
Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3909-3913
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 21 2018

Fingerprint Dive into the research topics of 'Importance Sampling Estimator of Outage Probability under Generalized Selection Combining Model'. Together they form a unique fingerprint.

Cite this