REVIEW
Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
classification
eess.SPcs.LG
keywords
opticalautoencoderfaultsnetworkspassiveaccuracyapproachconventional
read the original abstract
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.
Discussion (0). Continue with ORCID to comment.