mlsanityData to 7 Oct 2026

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

arXiv 2203.11727 ↗First posted 19 Mar 2022eess.SP cs.LGcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 1 of its 6 references that are in the map. First posted on arXiv 19 Mar 2022.

Builds on

1 of 6 references.

References · 1

  1. 2022A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosiscited by 3

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Cited by year

Citing papers per year: 2023: 3.

3 papers.

  1. 2023Branch Identification in Passive Optical Networks using Machine Learningcited by 1
  2. 2023Faulty Branch Identification in Passive Optical Networks using Machine Learningcited by 1
  3. 2023Fault Monitoring in Passive Optical Networks using Machine Learning Techniquescited by 1