Efficient Estimation of the Left Tail of Bimodal Distributions with Applications to Underwater Optical Communication Systems

TL;DR

This paper introduces efficient importance sampling methods to accurately estimate the probability of outages in underwater optical communication systems affected by turbulence, significantly reducing the number of samples needed compared to traditional methods.

Contribution

It presents novel importance sampling estimators optimized via cross-entropy for evaluating outage probabilities in complex underwater optical fading models.

Findings

Importance sampling reduces sample size by orders of magnitude.

Cross-entropy optimization effectively finds optimal biased distributions.

Method applies to exponential-lognormal and exponential-Gamma fading models.

Abstract

In this paper, we propose efficient importance sampling estimators to evaluate the outage probability of maximum ratio combining receivers over turbulence-induced fadings in underwater wireless optical channels. We consider two fading models: exponential-lognormal, and exponential-generalized Gamma. The cross-entropy optimization method is used to determine the optimal biased distribution. We show by simulations that the number of samples required by importance sampling estimator is much less compared to naive Monte Carlo for the same accuracy requirement.