Version 1
: Received: 18 May 2024 / Approved: 20 May 2024 / Online: 20 May 2024 (13:06:23 CEST)
How to cite:
Abdula, S. P.; Llagas, M. J.; Fernandez, A. M.; Arboleda, E. Machine Learning Applications for Fault Tracing and Localization in Optical Fiber Communication Networks: A Review. Preprints2024, 2024051285. https://doi.org/10.20944/preprints202405.1285.v1
Abdula, S. P.; Llagas, M. J.; Fernandez, A. M.; Arboleda, E. Machine Learning Applications for Fault Tracing and Localization in Optical Fiber Communication Networks: A Review. Preprints 2024, 2024051285. https://doi.org/10.20944/preprints202405.1285.v1
Abdula, S. P.; Llagas, M. J.; Fernandez, A. M.; Arboleda, E. Machine Learning Applications for Fault Tracing and Localization in Optical Fiber Communication Networks: A Review. Preprints2024, 2024051285. https://doi.org/10.20944/preprints202405.1285.v1
APA Style
Abdula, S. P., Llagas, M. J., Fernandez, A. M., & Arboleda, E. (2024). Machine Learning Applications for Fault Tracing and Localization in Optical Fiber Communication Networks: A Review. Preprints. https://doi.org/10.20944/preprints202405.1285.v1
Chicago/Turabian Style
Abdula, S. P., Alessandra May Fernandez and Edwin Arboleda. 2024 "Machine Learning Applications for Fault Tracing and Localization in Optical Fiber Communication Networks: A Review" Preprints. https://doi.org/10.20944/preprints202405.1285.v1
Abstract
The review aims to assess fifteen (15) academic literature sources, highlighting the application of machine learning algorithms in the maintenance operations of optical fiber networks. It exhibits the collection of data using PRISMA methodology—Preferred Reporting Item for Systems Review and Meta-Analyses. The application, results, and performance metrics are discussed based on the collected observations, computations, and statistics in the studies, which revealed records of high accuracy degrees ranging from 86% to 98% on average and quality ML models including Neural Networks (NNs), Support Vector Machines (SVMs), and LSTM, as well as deep learning models that disclosed effective results of determining challenges and problems within the optical fiber lines. The review mainly centralized on superior machine learning technologies that surpass traditional techniques in fault detection and localization for improved optical fiber networks’ operations while providing insights into the limitations and challenges encountered in real-world applications of these models, offering a comprehensive perspective on the optical fiber network’s domain.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
