Probability densities of second-order polarization mode dispersion including polarization dependent chromatic fiber dispersion

Foschini, G.J.; Nelson, L.E.; Jopson, R.M.; Kogelnik, H.

doi:10.1109/68.826918

Cited by 106 publications

(54 citation statements)

References 15 publications

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“…The individual samples are combined by sorting the values of DGD and/or second-order PMD obtained from all of the simulations into 80 bins and adjusting the contribution of each individual sample for the bias using the weights and likelihood ratios, as explained previously. The solid lines show the analytical pdfs in the long-length regime [36], while the dashed lines in Fig. 6 show exact and asymptotic solutions from [34] for the case of scramblers.…”

Section: B Probability Densities Of First-and Second-order Pmdmentioning

confidence: 99%

“…This is usually done with MC simulations and sorting the results into bins. The desired result is more than a single quantity; more precisely, we are trying to simultaneously estimate all the integrals (36) where is the quantity of interest, , with being the bin size, and . The integrals in (36) are of the same type as (1); thus, we can apply the IS techniques presented in Section II.…”

Section: A Numerical Calculation Of Probability Densitiesmentioning

confidence: 99%

“…The desired result is more than a single quantity; more precisely, we are trying to simultaneously estimate all the integrals (36) where is the quantity of interest, , with being the bin size, and . The integrals in (36) are of the same type as (1); thus, we can apply the IS techniques presented in Section II. It is clear, however, that in general no single biasing distribution can efficiently generate all the possible values of (e.g., see Fig.…”

Section: A Numerical Calculation Of Probability Densitiesmentioning

confidence: 99%

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Importance sampling for polarization-mode dispersion

Biondini

Kath

Menyuk

2002

IEEE Photon. Technol. Lett.

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Abstract-The basic theory of importance sampling (IS) as relevant to polarization-mode dispersion (PMD) in optical fibers is discussed, and its application to Monte Carlo (MC) simulations of PMD-induced transmission impairments is demonstrated. The use of IS allows rare PMD events to be simulated much more efficiently than with standard MC methods. As a consequence, methods employing IS provide natural and effective tools to assess PMD-induced impairments and outages in optical transmission systems at realistic probability levels.Index Terms-Importance sampling (IS), Monte Carlo (MC) simulations, optical fiber communications, polarization-mode dispersion (PMD).

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Section: B Probability Densities Of First-and Second-order Pmdmentioning

confidence: 99%

Section: A Numerical Calculation Of Probability Densitiesmentioning

confidence: 99%

Section: A Numerical Calculation Of Probability Densitiesmentioning

confidence: 99%

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Importance sampling for polarization-mode dispersion

Biondini

Kath

Menyuk

2002

IEEE Photon. Technol. Lett.

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“…In the companion paper [3], it was explained in detail that the joint-characteristic function can be applied to the firstand second-order PMD vectors for highly-birefringent fibers in long distance limit. This function has played a key role in subsequent papers to describe various probability distribution characteristics of PMD vectors [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Derivation of the Foschini and Shepp's Joint-Characteristic Function for the First-and Second-Order Polarization-Mode-Dispersion Vectors Using the Fokker-Planck Method

Lee

2008

Journal of the Optical Society of Korea

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“…13(b) and (c), respectively. The second-order PMD component associated with the polarization-dependent chromatic dispersion (PCD) [23] is represented in Fig. 13(d).…”

Section: B Emulator Based On Pmfs and Fiber-coil-based Pcsmentioning

confidence: 99%

Uniform Polarization Scattering With Fiber-Coil-Based Polarization Controllers

Muga

Pinto

Ferreira

et al. 2006

J. Lightwave Technol.

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Abstract-A detailed study of fiber-coil-based polarization controllers (PCs) is performed. First, a method to deterministically calculate the PC configuration in order to transform between any two states of polarization is presented. In a second stage, the case in which the configuration angles are randomly changed is studied. The cases of a single PC and of the system obtained with the concatenation of several PCs are analyzed. For both cases, a general expression for the variance of the Stokes parameters is obtained. Using this expression, it is demonstrated that it is possible to achieve uniform polarization scattering using a concatenation of fiber-coil-based PCs. Finally, it is shown that fiber-coil-based PCs can be used to emulate both first-and second-order polarizationmode dispersions.

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Probability densities of second-order polarization mode dispersion including polarization dependent chromatic fiber dispersion

Cited by 106 publications

References 15 publications

Importance sampling for polarization-mode dispersion

Importance sampling for polarization-mode dispersion

Derivation of the Foschini and Shepp's Joint-Characteristic Function for the First-and Second-Order Polarization-Mode-Dispersion Vectors Using the Fokker-Planck Method

Uniform Polarization Scattering With Fiber-Coil-Based Polarization Controllers

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