Dynamic mitigation of EDFA power excursions with machine learning.
Opt Express
; 25(3): 2245-2258, 2017 Feb 06.
Article
en En
| MEDLINE
| ID: mdl-29519073
Dynamic optical networking has promising potential to support the rapidly changing traffic demands in metro and long-haul networks. However, the improvement in dynamicity is hindered by wavelength-dependent power excursions in gain-controlled erbium doped fiber amplifiers (EDFA) when channels change rapidly. We introduce a general approach that leverages machine learning (ML) to characterize and mitigate the power excursions of EDFA systems with different equipment and scales. An ML engine is developed and experimentally validated to show accurate predictions of the power dynamics in cascaded EDFAs. Recommended channel provisioning based on the ML predictions achieves within 1% error of the lowest possible power excursion over 94% of the time. We also showcase significant mitigation of EDFA power excursions in super-channel provisioning when compared to the first-fit wavelength assignment algorithm.
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01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Opt Express
Asunto de la revista:
OFTALMOLOGIA
Año:
2017
Tipo del documento:
Article
Pais de publicación:
Estados Unidos