Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks
arXiv:2203.11727v110 citationsh-index: 30
Originality Incremental advance
AI Analysis
This addresses fault diagnosis for passive optical networks, representing an incremental improvement with specific gains.
The paper tackles fiber fault diagnosis in passive optical networks by proposing a deep learning approach, achieving 97% detection accuracy and 0.18 m RMSE for localization, outperforming conventional methods.
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.