Field Trial of Machine-Learning-Assisted and SDN-Based Optical Network Management

Shuangyi Yan, Faisal Nadeem Khan, Alex Mavromatis, Qirui Fan, Hilary Frank, Reza Nejabati, Alan Pak Tao Lau, Dimitra Simeonidou

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
164 Downloads (Pure)

Abstract

In this paper, we reported machine-learning based network dynamic abstraction over a field-trial testbed. The implemented network-scale NCMDB allows the ML-based quality-of-transmission predictor abstract dynamic link parameters for further network planning.
Original languageEnglish
Title of host publication2019 Optical Fiber Communications Conference and Exhibition, OFC 2019 - Proceedings
PublisherOptical Society of America (OSA)
Number of pages3
ISBN (Electronic)9781943580538
DOIs
Publication statusPublished - 22 Apr 2019
Event2019 Optical Fiber Communications Conference and Exhibition, OFC 2019 - San Diego, United States
Duration: 3 Mar 20197 Mar 2019

Conference

Conference2019 Optical Fiber Communications Conference and Exhibition, OFC 2019
CountryUnited States
CitySan Diego
Period3/03/197/03/19

    Fingerprint

Cite this

Yan, S., Khan, F. N., Mavromatis, A., Fan, Q., Frank, H., Nejabati, R., Lau, A. P. T., & Simeonidou, D. (2019). Field Trial of Machine-Learning-Assisted and SDN-Based Optical Network Management. In 2019 Optical Fiber Communications Conference and Exhibition, OFC 2019 - Proceedings [M2E.1] Optical Society of America (OSA). https://doi.org/10.1364/OFC.2019.M2E.1