Self-learning approaches for real optical networks
Visualitza/Obre
Cita com:
hdl:2117/169769
Tipus de documentText en actes de congrés
Data publicació2019
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
ProjecteMETRO-HAUL - METRO High bandwidth, 5G Application-aware optical network, with edge storage, compUte and low Latency (EC-H2020-761727)
COGNITIVE 5G APPLICATION-AWARE OPTICAL METRO NETWORKS INTEGRATING MONITORING, DATA ANALYTICS AND OPTIMIZATION (AEI-TEC2017-90097-R)
COGNITIVE 5G APPLICATION-AWARE OPTICAL METRO NETWORKS INTEGRATING MONITORING, DATA ANALYTICS AND OPTIMIZATION (AEI-TEC2017-90097-R)
Abstract
Self-learning approaches to facilitate the deployment of ML algorithms in real networks are analyzed and their performance evaluated through an illustrative use case. Results show large benefits of collective self-learning with centralized retraining.
Descripció
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
CitacióRuiz, M. [et al.]. Self-learning approaches for real optical networks. A: Optical Fiber Communications Conference and Exposition. "Optical Fiber Communication Conference 2019, San Diego, California, United States, 3-7 March 2019". Institute of Electrical and Electronics Engineers (IEEE), 2019, article 8696619, p. 1-3.
ISBN978-1-943580-53-8
Versió de l'editorhttps://ieeexplore.ieee.org/document/8696619
Fitxers | Descripció | Mida | Format | Visualitza |
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[OFC] CollectiveLearning.pdf | 359,1Kb | Visualitza/Obre |