# Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks

arXiv: https://arxiv.org/abs/2203.11727 · HTML: https://mlsanity.com/paper/2203.11727/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 19 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis](https://mlsanity.com/paper/2202.08034/) (2022)

## Built on by (most cited first, 3 of 3)

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

## Abstract (arXiv)

We propose a deep learning approach based on an autoencoder for identifying and localizing fiber faults in passive optical networks. The experimental results show that the proposed method detects faults with 97% accuracy, pinpoints them with an RMSE of 0.18 m and outperforms conventional techniques.