# Fault Monitoring in Passive Optical Networks using Machine Learning Techniques

arXiv: https://arxiv.org/abs/2307.03945 · HTML: https://mlsanity.com/paper/2307.03945/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks](https://mlsanity.com/paper/2203.11727/) (2022)
- [Branch Identification in Passive Optical Networks using Machine Learning](https://mlsanity.com/paper/2304.00285/) (2023)
- [Faulty Branch Identification in Passive Optical Networks using Machine Learning](https://mlsanity.com/paper/2304.01376/) (2023)

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

- [Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2405.09557/) (2024)

## Abstract (arXiv)

Passive optical network (PON) systems are vulnerable to a variety of failures, including fiber cuts and optical network unit (ONU) transmitter/receiver failures. Any service interruption caused by a fiber cut can result in huge financial losses for service providers or operators. Identifying the faulty ONU becomes difficult in the case of nearly equidistant branch terminations because the reflections from the branches overlap, making it difficult to distinguish the faulty branch given the global backscattering signal. With increasing network size, the complexity of fault monitoring in PON systems increases, resulting in less reliable monitoring. To address these challenges, we propose in this paper various machine learning (ML) approaches for fault monitoring in PON systems, and we validate them using experimental optical time domain reflectometry (OTDR) data.