Statistics of Polarization-Mode Dispersion Emulators with Unequal Sections

Stone, Brenton R.; Biondini, Gino; Kath, William L.

doi:10.1137/070696350

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…In the last ten years, various techniques have been used to efficiently simulate system outages caused by PMD, including the multicanonical Monte Carlo (MMC) method [33] and a Markov chain Monte Carlo (MCMC) method [28]. In particular, importance sampling (IS) has been used with success in a number of system configurations [3,4,5,26,30]. Recently, a method that combines IS with the cross entropy method (CE) [13,17,27] has also been developed to study a specific model of PMD [23], and a similar combination was used in [14] to study noise-induced perturbations of optical solitons.…”

Section: Introduction Polarization-mode Dispersion (Pmd)mentioning

confidence: 99%

On the Efficiency of Importance Sampling Techniques for Polarization-Mode Dispersion in Optical Fiber Transmission Systems

Marzec¹,

Schuster²,

Biondini³

2013

SIAM J. Appl. Math.

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An adaptive variance reduction technique was recently proposed to efficiently study transmission errors induced by polarization-mode dispersion (PMD) in optical fiber transmission systems. The technique combines importance sampling (IS) and the cross entropy (CE) method to bias Monte Carlo simulations. Here we (i) present a detailed study to quantify the computational efficiency of this technique, (ii) introduce a novel variant of the hinge model for PMD in optical fiber communication systems, and (iii) use the IS-CE technique to compare the statistical properties of various hinge models. Specifically, we compare the statistics of PMD-induced transmission impairments due to the traditional hinge model (isotropic hinge output), the waveplate hinge model (anisotropic hinge output), and the novel variant of the waveplate hinge model introduced here. We use computationally efficient expressions to determine the probability density function of the differential group delay, and we couple these techniques with the outage map method to compute statistical probabilities of system outages, as quantified by the noncompliant capacity ratio.

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