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Optical and Quantum Electronics

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01-01-2024

Machine learning-based inverse design of raised cosine few mode fiber for low coupling

Authors: Saleh Chebaane, Sana Ben Khalifa, Maher Jebali, Ali Louati, Haythem Bahri, Alaa Dafhalla

Published in: Optical and Quantum Electronics | Issue 1/2024

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Abstract

In mode division multiplexing techniques, few-mode fiber (FMF) with multiple modes and low coupling is the demanded design. The design of FMF become more complex due to different parameters and shape design of it profile especially when you need to get a design with high number of modes, and it will consume a lot of computing time. Therefore this paper propose a machine learning technique employ neural network to design FMF raised cosine profile inversely. We realize the inverted design of raised cosine with U refractive index layer FMFs for supporting 6 mode operation, by adopting the minimal index difference between neighboring modes to get low coupling between modes. This proposed strategy gives higher precision with less complex design of FMF, in addition to low coupling between propagating modes. This promoting method can be applied in other types of fiber optic profile which it need a lot of parameters optimization.

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Title: Machine learning-based inverse design of raised cosine few mode fiber for low coupling
Authors: Saleh Chebaane
Sana Ben Khalifa
Maher Jebali
Ali Louati
Haythem Bahri
Alaa Dafhalla
Publication date: 01-01-2024
Publisher: Springer US
Published in: Optical and Quantum Electronics / Issue 1/2024
Print ISSN: 0306-8919
Electronic ISSN: 1572-817X
DOI: https://doi.org/10.1007/s11082-023-05695-8

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