Supervised Machine Learning Techniques for Quality of Transmission Assessment in Optical Networks | IEEE Conference Publication | IEEE Xplore

Supervised Machine Learning Techniques for Quality of Transmission Assessment in Optical Networks


Abstract:

We propose and compare a number of machine learning models to classify unestablished lightpaths into high or low quality of transmission (QoT) categories in impairment-aw...Show More

Abstract:

We propose and compare a number of machine learning models to classify unestablished lightpaths into high or low quality of transmission (QoT) categories in impairment-aware wavelength-routed optical networks. The performance of these models is evaluated in long haul communication networks and compared to previous proposals. Results show that, especially random forests and bagging trees approaches, significantly reduce the required computing time to classify the QoT of a given lightpath, while accuracy remains around 99.9%.
Date of Conference: 01-05 July 2018
Date Added to IEEE Xplore: 27 September 2018
ISBN Information:
Electronic ISSN: 2161-2064
Conference Location: Bucharest, Romania

References

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