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Branch Identification in Passive Optical Networks using Machine Learning

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Abstract

A machine learning approach for improving monitoring in passive optical networks with almost equidistant branches is proposed and experimentally validated. It achieves a high diagnostic accuracy of 98.7% and an event localization error of 0.5m.

© 2023 The Author(s)

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