Journal article Open Access

Modeling EDFA Gain Ripple and Filter Penalties with Machine Learning for Accurate QoT Estimation

Mahajan, Ankush; Christodoulopoulos, Konstantinos; Martínez, Ricardo; Spadaro, Salvatore; Muñoz, Raül

For reliable and efficient network planning and operation, accurate estimation of Quality of Transmission (QoT) before establishing or reconfiguring the connection is necessary. In optical networks, a design margin is generally included in a QoT estimation tool (Qtool) to account for modeling and parameter inaccuracies, ensuring the acceptable performance. In this article, we use monitoring information from an operating network combined with supervised machine learning (ML) techniques to understand the network conditions. In particular, we model the penalties generated due to i) Erbium Doped Fiber Amplifier (EDFA) gain ripple effect, and ii) filter spectral shape uncertainties at Reconfigurable Optical Add and Drop Multiplexer (ROADM) nodes. Enhancing the Qtool with the proposed ML regression models yields estimates for new or reconfigured connections that account for these two effects, resulting in more accurate QoT estimation and a reduced design margin. We initially propose two supervised ML regression models, implemented with Support Vector Machine Regression (SVMR), to estimate the individual penalties of the two effects and then a combined model. On Deutsche Telekom (DT) network topology with 12 nodes and 40 bidirectional links, we achieve a design margin reduction of ~1 dB for new connection requests.

@ 2020 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.
Files (1.7 MB)
Name Size
Modeling EDFA Gain Ripple and Filter.pdf
md5:21c51b340435abd40fb73256ac287428
1.7 MB Download
9
16
views
downloads
Views 9
Downloads 16
Data volume 27.2 MB
Unique views 7
Unique downloads 15

Share

Cite as