Abstract

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.

© 2018 The Author(s)