Importance Sampling Estimator of Outage Probability under Generalized Selection Combining Model
Online Publication Date2018-09-21
Print Publication Date2018-04
Permanent link to this recordhttp://hdl.handle.net/10754/630800
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AbstractWe 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.
CitationRached, N. B., Botev, Z., Kammoun, A., Alouini, M.-S., & Tempone, R. (2018). Importance Sampling Estimator of Outage Probability under Generalized Selection Combining Model. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp.2018.8462177
Conference/Event name2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)