2018 Asia Communications and Photonics Conference (ACP) · 2018 · 12 citations · 5 references
ComputingComputer EngineeringComputer ScienceFault ToleranceFault-tolerant ComputingMachine LearningClassificationError DetectionFault DetectionNetwork ReliabilityOptical Communication SystemsPattern RecognitionBit Error RateBit-error Rate AnalysisClassification AccuracyClassification Accuracy AssessmentK-nearest NeighborsMachine Learning For Fault DetectionNearest-neighbor ClassificationOptical Networking
Proactive control strategies based on machine learning can be expected to be very useful for detecting anomalies and soft failures in high-capacity optical networks. In this paper, we propose a bit error rate (BER) classifier based on the K-nearest neighbors (KNN) algorithm. The BER classifier has been used for classifying field BER data collected in a 230-km optical link of the CANARIE network. The results show that the classification accuracy increases up to 97.8% depending on the features considered in the classification process.
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