Deep learning-based fault diagnosis and localization method for fiber optic cables in communication networks
Author:
Affiliation:
1. 1 Information and Communication Branch of State Grid of Shanxi Electric Power Company , Taiyuan , Shanxi , , China
2. 2 State Grid of Shanxi Electric Power Company , Taiyuan , Shanxi , , China .
Abstract
Publisher
Walter de Gruyter GmbH
Subject
Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science
Link
https://www.sciendo.com/pdf/10.2478/amns.2023.2.00241
Reference17 articles.
1. Fehenberger, T. (2017). Information rates of probabilistically shaped coded modulation for a multi-span fiber-optic communication system with 64QAM. Optics Communications.
2. Hger, C., & Pfister, H. D. (2020). Physics-Based Deep Learning for Fiber-Optic Communication Systems. IEEE Journal on Selected Areas in Communications.
3. Glazov, A. I., Grigor’Ev, V. V., Kravtsov, V. E., et al. (2018). International Comparisons of Standards in the Area of Fiber-Optic Communication and Information Transmission Systems. Measurement Techniques.
4. Venkitesh, D. (2022). Changing phases of fiber optic communication. Journal of Optics.
5. Venu, D., Mayuri, A., Neelakandan, S., et al. (2022). An efficient low complexity compression based optimal homomorphic encryption for secure fiber optic communication. Optik.
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