Name
Abstract | ||
|---|---|---|
Estimating lightpath Quality of Transmission (QoT) is crucial in network design and service provisioning. Recent studies have turned to Machine Learning (ML) techniques to improve the accuracy of QoT estimation. We distinguish two categories of solutions: the first category aims to build ML-based QoT estimation models that outperform the analytical model while the second category uses ML algorithms to reduce uncertainties on parameters provided as input to analytical model. In this overview, we describe the solutions in each category and discuss their practical feasibility and added benefit for operational networks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICTON51198.2020.9203755 | 2020 22ND INTERNATIONAL CONFERENCE ON TRANSPARENT OPTICAL NETWORKS (ICTON 2020) |
Keywords | DocType | ISSN |
machine learning, WDM networks, QoT | Conference | 2162-7339 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Ayassi | 1 | 0 | 0.34 |
| Ahmed Triki | 2 | 0 | 1.69 |
| M. Laye | 3 | 0 | 0.34 |
| Noël Crespi | 4 | 626 | 80.05 |
| Roberto Minerva | 5 | 0 | 0.34 |
| Clara Catanese | 6 | 0 | 0.68 |