mlsanityData to 7 Oct 2026

Branch Identification in Passive Optical Networks using Machine Learning

arXiv 2304.00285 ↗First posted 1 Apr 2023cs.LG cs.NI eess.SPcited by 1

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Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 2 of its 7 references that are in the map. First posted on arXiv 1 Apr 2023.

Builds on

2 of 7 references.

References · 2

  1. 2022Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networkscited by 3
  2. 2022A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communicationcited by 1

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Fault Monitoring in Passive Optical Networks using Machine Learning Techniquescited by 1