Skip to Main content Skip to Navigation
Conference papers

Machine Learning Assisted Quality of Transmission Estimation and Planning with Reduced Margins

Abstract : In optical transport networks, the Quality of Transmission (QoT) using a physical layer model (PLM) is estimated before establishing new or reconfiguring established optical connections. Traditionally, high margins are added to account for the model’s inaccuracy and the uncertainty in the current and evolving physical layer conditions, targeting uninterrupted operation for several years, until the end-of-life (EOL). Reducing the margins increases network efficiency but requires accurate QoT estimation. We present two machine learning (ML) assisted QoT estimators that leverage monitoring data of existing connections to understand the actual physical layer conditions and achieve high estimation accuracy. We then quantify the benefits of planning/upgrading a network over multiple periods with accurate QoT estimation as opposed to planning with EOL margins.
Complete list of metadata

https://hal.inria.fr/hal-03200661
Contributor : Hal Ifip <>
Submitted on : Friday, April 16, 2021 - 5:06:43 PM
Last modification on : Friday, April 16, 2021 - 5:38:31 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Konstantinos Christodoulopoulos, Ippokratis Sartzetakis, Polizois Soumplis, Emmanouel Varvarigos. Machine Learning Assisted Quality of Transmission Estimation and Planning with Reduced Margins. 23th International IFIP Conference on Optical Network Design and Modeling (ONDM), May 2019, Athens, Greece. pp.211-222, ⟨10.1007/978-3-030-38085-4_19⟩. ⟨hal-03200661⟩

Share

Metrics

Record views

13