Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • 2015 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference
  • (Optica Publishing Group, 2015),
  • paper CE_1_6

Supermode interference analysis as an effective method of crosstalk examination in multicore fibers

Not Accessible

Your library or personal account may give you access

Abstract

Multicore fibers (MCFs) are considered as a successor of standard single mode fibers, which will increase the bandwidth capacity of existing optical fiber networks [1]. One of the most important parameters of MCFs from the point of view of telecommunication applications is crosstalk (XT), which describes optical power transfer between cores. There are different methods of decreasing XT in MCFs, such as increasing distance between cores [2], differentiating refractive indices of cores [3], employing refractive index tranches [4] or air holes [5] around cores. To design properly MCF a reliable and flexible method of XT analysis is required. In the paper we report a novel concept of description and examination of XT in complex MCF structures, which take into account not only the intensity of power transfer but also its periodicity.

© 2015 IEEE

PDF Article
More Like This
Coupled Mode Analysis of Crosstalk in Multicore Fiber with Random Perturbations

Ming-Jun Li, Shenping Li, and Robert A. Modavis
W2A.35 Optical Fiber Communication Conference (OFC) 2015

Analysis of Stimulated Raman Scattering and Four-Wave Mixing Effects on Crosstalk of Multicore Fibers

D. E. Ceballos-Herrera, R. Gutierrez-Castrejón, and J. J. Sánchez-Mondragón
JW4A.56 Frontiers in Optics (FiO) 2017

Supermode Interference in Multicore Fiber Optimized for use in Sensing Applications

A. Van Newkirk, E. Antonio-Lopez, G. Salceda-Delgado, R. Amezcua-Correa, and A. Schülzgen
SoW2B.4 Specialty Optical Fibers (SOF) 2014

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.