Cisco Technology, Inc.

Enrico Sozio

Pietro Invernizzi

Stefano Piciaccia

Alberto Arnaldo Tanzi

Giacomo Boracchi

Luca Magri

Prasenjeet Acharjee

Cesare Alippi

Antonino Maria Rizzo

In some aspects, the techniques described herein relate to a method that includes: obtaining optical channel spectrum data that includes amplified spontaneous emission data and channel data associated with optical signals propagated through an optical fiber; fitting an amplified spontaneous emission trend to the amplified spontaneous emission data; fitting a channel trend to the channel data; jointly optimizing the amplified spontaneous emission trend and the channel trend to determine an optimized channel trend; and determining an anomaly in the channel data based upon the optimized channel trend.

Automatic trend identification and anomaly detection in optical channel monitoring spectrum

In one embodiment, a device receives optical time domain reflectometer (OTDR) trace samples, each sample labeled with an associated fiber optic cable condition. The device alters the received OTDR trace samples to generate a set of synthetic OTDR trace samples. Each synthetic sample is labeled with the label of the received sample that was altered to generate the synthetic sample. The device trains a machine learning-based classifier using a training dataset that comprises the synthetic OTDR trace samples. The device uses the trained classifier to identify a condition along a particular fiber optic cable based on OTDR trace data obtained from that cable.

Optical time domain reflectometer (OTDR)-based classification for fiber optic cables using machine learning

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Monza, Italy

Enrico Sozio