Smart optical networks enabled by performance monitoring and machine learning
Research Project , 2021 – 2024

The two main objectives of the project are:

(1) investigating machine learning (ML) techniques for quality of transmission (QoT) estimation and prediction in the optical layer;

(2) exploring ML methods for anomaly detection and proactive failure management at the network level.

ML-based methods for QoT estimation and prediction at the physical layer will be explored. ML techniques such as long-term short-memory (LSTM) and neural networks will be investigated for QoT prediction as ways to discover patterns in time-series and achieve dynamic and autonomous system margin control.

Participants

Lena Wosinska (contact)

Forskningsprofessor at Chalmers, Electrical Engineering, Communication and Antenna Systems, Optical Networks

Marija Furdek Prekratic

Assistant Professor at Chalmers, Electrical Engineering, Communication and Antenna Systems, Optical Networks

Funding

National Research Council Canada

Funding Chalmers participation during 2021–2024

École de Technologie Supérieure (ÉTS)

Funding Chalmers participation during 2021–2024

More information

Latest update

2021-10-01