Скачать или смотреть Machine-learning Method for Quality of Transmission Prediction of Unestablished Lightpaths

IEEE ComSoc selected publications: “Machine-learning method for quality of transmission prediction of unestablished lightpaths”, by Cristina Rottondi, Luca Barletta, Alessandro Giusti, and Massimo Tornatore. IEEE/OSA Journal of Optical Communications and Networking, vol. 10, no. 2, pp. A286-A297, Feb. 2018.

Predicting the quality of transmission (QoT) of a lightpath prior to its deployment is a step of capital importance for an optimized design of optical networks. Due to the continuous advances in optical transmission, the number of design parameters available to system engineers (e.g., modulation formats, baud rate, code rate, etc.) is growing dramatically. In this work, we investigate a ML classifier that predicts whether the bit error rate of unestablished lightpaths meets the required system threshold based on traffic volume, desired route, and modulation format.

The whole paper may be found here:
https://ieeexplore.ieee.org/document/...