Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Lightwave Technology
  • Vol. 31,
  • Issue 16,
  • pp. 2976-2980
  • (2013)

Temporal Autocorrelation Functions of PMD Variables in the Anisotropic Hinge Model

Not Accessible

Your library or personal account may give you access

Abstract

We present analytic expressions for the temporal autocorrelation functions (ACF's) of the polarization mode dispersion (PMD) vector, the squared differential group delay (DGD) and the state of polarization (SOP) in the hinge model for stochastically varying hinges. We also derive the continuous limit of the temporal ACF of the squared DGD. Our studies demonstrate that for large time offsets, the ACF of the PMD vector approaches a constant value that depends principally on the DGD of the last fiber section but is also affected to a diminishing degree by the DGD of preceding fiber sections. We also show that sinusoidal perturbations of the hinge rotation angles do not significantly alter the results. The accuracy of the procedure is further established through comparison with numerical simulations.

© 2013 IEEE

PDF Article
More Like This
PMD correlation properties in the hinge model

George Soliman and David Yevick
J. Opt. Soc. Am. A 30(3) 380-384 (2013)

Anisotropic hinge model for polarization-mode dispersion in installed fibers

Jinglai Li, Gino Biondini, William L. Kath, and Herwig Kogelnik
Opt. Lett. 33(16) 1924-1926 (2008)

Autocorrelation function of the polarization-mode dispersion vector

Magnus Karlsson and Jonas Brentel
Opt. Lett. 24(14) 939-941 (1999)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.