# Branch Identification in Passive Optical Networks using Machine Learning

arXiv: https://arxiv.org/abs/2304.00285 · HTML: https://mlsanity.com/paper/2304.00285/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 1 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks](https://mlsanity.com/paper/2203.11727/) (2022)
- [A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication](https://mlsanity.com/paper/2211.02842/) (2022)

## Built on by (most cited first, 1 of 1)

- [Fault Monitoring in Passive Optical Networks using Machine Learning Techniques](https://mlsanity.com/paper/2307.03945/) (2023)

## 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