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Associating machine-learning and analytical models for quality of transmission estimation: combining the best of both worlds.

, , and . JOCN, 13 (6): C21-C30 (2021)

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Accurate QoT Estimation by Means of a Reduction of EDFA Characteristics Uncertainties with Machine Learning., , and . ONDM, page 1-3. IEEE, (2020)Impact of tunability and blocking fabric on optical slot switching ring performance., and . ICTON, page 1-4. IEEE, (2016)Cross-layer adaptive routing and wavelength assignment in all-optical networks., , , and . IEEE J. Sel. Areas Commun., 26 (6-Supplement): 32-44 (2008)Introduction to the JOCN Special Issue on Machine Learning Applied to QoT Estimation in Optical Networks., , , and . JOCN, 13 (4): MLAQ1-MLAQ2 (2021)Associating machine-learning and analytical models for quality of transmission estimation: combining the best of both worlds., , and . JOCN, 13 (6): C21-C30 (2021)Introduction to the JOCN special issue on low-margin optical networks: publisher's note., , , and . JOCN, 11 (12): 598 (2019)Multicast in mobile backhaul with optical packet ring., , , and . WiMob, page 181-188. IEEE Computer Society, (2013)High data rate coherent optical slot switched networks: a practical and technological perspective., , , , and . IEEE Communications Magazine, 53 (8): 124-129 (2015)Impact of the Electronic Architecture of Optical Slot Switching Nodes on Latency in Ring Networks., , , and . JOCN, 6 (8): 718-729 (2014)Optimized Monitor Placement for Accurate QoT Assessment in Core Optical Networks., , , , and . JOCN, 4 (1): 15-24 (2012)