Investigation of low crosstalk heterogeneous weakly coupled multicore fiber with standard cladding diameter
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Umar Farooque
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Veer Chandra
Abstract
In this work, standard cladding diameter based weakly coupled heterogeneous multicore fibers (SC-WC-Het-MCFs) have been investigated using COMSOL and MATLAB simulation platforms to obtain low crosstalk (XT) and peak bending radius (R pk ) values. Therefore, fiber design parameters dependent fluctuations in the crosstalk and worst crosstalk (XTW) levels have been analyzed for the 6-core and 8-core MCFs arranged in rectangular, square, circular and double ring layouts. Further, for all the considered MCF structures, in order to obtain mode effective area value, A eff ≈80 μm2, core parameters, peak bending radius and crosstalk values have been obtained. The lowest values of the crosstalk have been obtained as −39.13 dB and −18.18 dB in the circular layout, respectively for the 6-core and 8-core SC-WC-Het-MCFs. Further, compared to the 6-core and 8-core double ring layout SC-WC-Het-MCFs, the value of the R pk for the circular layout is slightly higher in case of 6-core, while it is significantly lower in case of 8-core SC-WC-Het-MCF. Thus, compared to the double ring structure reported in the literature, the circular arrangement of cores in SC-WC-Het-MCF results in low XT and R pk and is simple in design. Hence, the low XT and R pk of the circular layout SC-WC-Het-MCF may be suitable for the long-distance transmission applications.
Acknowledgments
We thanks to Muzaffarpur Institute of Technology, Muzaffarpur, Bihar, India and Department of ECE, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamilnadu, India for providing the immense support and encouragement.
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Research ethics: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Informed consent: Not applicable.
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Conflict of interest: The authors state no conflict of interest.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Research funding: None declared.
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Data availability: Not applicable.
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