TY - JOUR

T1 - On the Sum of Order Statistics and Applications to Wireless Communication Systems Performances

AU - Ben Rached, Nadhir

AU - Botev, Zdravko

AU - Kammoun, Abla

AU - Alouini, Mohamed-Slim

AU - Tempone, Raul

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2018/9/26

Y1 - 2018/9/26

N2 - We consider the problem of evaluating the cumulative distribution function (CDF) of the sum of order statistics, which serves to compute outage probability (OP) values at the output of generalized selection combining receivers. Generally, closed-form expressions of the CDF of the sum of order statistics are unavailable for many practical distributions. Moreover, the naive Monte Carlo (MC) method requires a substantial computational effort when the probability of interest is sufficiently small. In the region of small OP values, we instead propose two effective variance reduction techniques that yield a reliable estimate of the CDF with small computing cost. The first estimator, which can be viewed as an importance sampling estimator, has bounded relative error under a certain assumption that is shown to hold for most of the challenging distributions. A possible improvement of this estimator is then proposed for the Pareto and the Weibull cases. The second is a conditional MC estimator that achieves the bounded relative error property for the generalized Gamma case and the logarithmic efficiency for the Log-normal case. Finally, the efficiency of these estimators is compared via various numerical simulations.

AB - We consider the problem of evaluating the cumulative distribution function (CDF) of the sum of order statistics, which serves to compute outage probability (OP) values at the output of generalized selection combining receivers. Generally, closed-form expressions of the CDF of the sum of order statistics are unavailable for many practical distributions. Moreover, the naive Monte Carlo (MC) method requires a substantial computational effort when the probability of interest is sufficiently small. In the region of small OP values, we instead propose two effective variance reduction techniques that yield a reliable estimate of the CDF with small computing cost. The first estimator, which can be viewed as an importance sampling estimator, has bounded relative error under a certain assumption that is shown to hold for most of the challenging distributions. A possible improvement of this estimator is then proposed for the Pareto and the Weibull cases. The second is a conditional MC estimator that achieves the bounded relative error property for the generalized Gamma case and the logarithmic efficiency for the Log-normal case. Finally, the efficiency of these estimators is compared via various numerical simulations.

UR - http://hdl.handle.net/10754/630799

UR - https://ieeexplore.ieee.org/abstract/document/8472928

UR - http://www.scopus.com/inward/record.url?scp=85054285932&partnerID=8YFLogxK

U2 - 10.1109/TWC.2018.2871201

DO - 10.1109/TWC.2018.2871201

M3 - Article

VL - 17

SP - 7801

EP - 7813

JO - IEEE Transactions on Wireless Communications

JF - IEEE Transactions on Wireless Communications

SN - 1536-1276

IS - 11

ER -