Impact of multi-vendor transponders performance on design margin in optical networks
For reliable and efficient network planning and operation, accurate estimation of Quality of Transmission (QoT) is necessary. In optical networks, a physical layer model (PLM) is typically used as a QoT estimation tool (Qtool) including a design margin to account for modeling and parameter inaccurac...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/354245 |
| Acesso em linha: | https://hdl.handle.net/2117/354245 https://dx.doi.org/10.1109/ACCESS.2021.3107296 |
| Access Level: | acceso abierto |
| Palavra-chave: | Optical communications Design Margin Optical Networks Quality Of Transmission (Qot) Transponders. Comunicacions òptiques Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| Resumo: | For reliable and efficient network planning and operation, accurate estimation of Quality of Transmission (QoT) is necessary. In optical networks, a physical layer model (PLM) is typically used as a QoT estimation tool (Qtool) including a design margin to account for modeling and parameter inaccuracies, to ensure acceptable performance. Such margin also covers the performance variations of the transponders (TPs) which are relatively low in a single vendor environment. However, for disaggregated networks that utilize TPs from multiple vendors, such as partial disaggregated networks with open line system (OLS), this traditional approach limits the Qtool estimation accuracy. Although higher TP performance variations can be covered with an additional margin, this approach would reduce the efficiency and consume the benefits of disaggregation. Therefore, we propose PLM extensions that capture the performance variations of multi- vendor TPs. In particular, we propose four TP vendor dependent performance factors and we also devise a Machine Learning (ML) scheme to learn these performance factors in offline and online network planning scenarios. The proposed extended PLM and ML training scheme are evaluated through realistic simulations. Results show a design margin reduction of greater than 1 dB for new connection requests in a disaggregated network with TPs from four vendors. On top of this, the results also show a ~0.5 dB additional Signal to Noise Ratio (SNR) saving for new connection requests by proper selection of the TPs. |
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