Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks
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 pinpoints them with an RMSE of 0.18 m and outperforms conventional techniques.
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