Estimating the quality of transmission (QoT) of a lightpath before its establishment is a critical procedure for efficient design and management of optical networks. Recently, supervised machine learning (ML) techniques for QoT estimation have been proposed as an effective alternative to wellestablished, yet approximated, analytic models that often require the introduction of conservative margins to compensate for model inaccuracies and uncertainties. Unfortunately, to ensure high estimation accuracy, the training set (i.e., the set of historical field data, or “samples,” required to train these supervised ML algorithms) must be very large, while in real network deployments, the number of monitored/monitorable lightpaths is limited by several practical considerations. This is especially true for lightpaths with an abovethreshold bit error rate (BER) (i.e., malfunctioning or wrongly dimensioned lightpaths), which are infrequently observed during network operation. Samples with abovethreshold BERs can be acquired by deploying probe lightpaths, but at the cost of increased operational expenditures and wastage of spectral resources. In this paper, we propose to use
 Award ID(s):
 2046393
 Publication Date:
 NSFPAR ID:
 10333719
 Journal Name:
 Information and Inference: A Journal of the IMA
 ISSN:
 20498772
 Sponsoring Org:
 National Science Foundation
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