We investigate a machine-learning technique that predicts whether the bit-errorrate of unestablished lightpaths meets the required threshold based on traffic volume, desired route and modulation format. The system is trained and tested on synthetic data.
QoT estimation for unestablished lighpaths using machine learning / Barletta, L.; Giusti, A.; Rottondi, C.; Tornatore, M.. - ELETTRONICO. - (2017), pp. 1-3. (Intervento presentato al convegno 2017 Optical Fiber Communications Conference and Exhibition, OFC 2017 tenutosi a Los Angeles, CA (USA) nel 19-23 March 2017).
QoT estimation for unestablished lighpaths using machine learning
Barletta L.;Rottondi C.;
2017
Abstract
We investigate a machine-learning technique that predicts whether the bit-errorrate of unestablished lightpaths meets the required threshold based on traffic volume, desired route and modulation format. The system is trained and tested on synthetic data.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2722694
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