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| Main Authors: | , , , , , , , |
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| Format: | Recurso digital |
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Zenodo
2022
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| Online Access: | https://doi.org/10.5281/zenodo.14615414 |
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| _version_ | 1866902215615053824 |
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| author | Arpanaei, Farhad Shariati, Behnam Safari, Pooyan Ranjbar Zefreh, Mahdi Hernández, José Alberto Carena, Andrea Fischer, Johannes LARRABEITI, DAVID |
| author_facet | Arpanaei, Farhad Shariati, Behnam Safari, Pooyan Ranjbar Zefreh, Mahdi Hernández, José Alberto Carena, Andrea Fischer, Johannes LARRABEITI, DAVID |
| contents | <p>We propose a novel approach to perform QoT estimation relying on joint exploitation of machine learning and analytical formula that offers accurate estimation when applied to scenarios with heterogeneous span profiles and sparsely occupied links. Our approach significantly outperforms the widely used lightpath-level QoT estimation.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14615414 |
| institution | Zenodo |
| language | |
| publishDate | 2022 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Novel Approach for Joint Analytical and ML-assisted GSNR Estimation in Flexible Optical Network Arpanaei, Farhad Shariati, Behnam Safari, Pooyan Ranjbar Zefreh, Mahdi Hernández, José Alberto Carena, Andrea Fischer, Johannes LARRABEITI, DAVID <p>We propose a novel approach to perform QoT estimation relying on joint exploitation of machine learning and analytical formula that offers accurate estimation when applied to scenarios with heterogeneous span profiles and sparsely occupied links. Our approach significantly outperforms the widely used lightpath-level QoT estimation.</p> |
| title | A Novel Approach for Joint Analytical and ML-assisted GSNR Estimation in Flexible Optical Network |
| url | https://doi.org/10.5281/zenodo.14615414 |