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EnglishWith the advancement in evolving concepts of software-defined networks and elastic-optical-network, the number of design parameters is growing dramatically, making the lightpath (LP) deployment more complex. Typically, worst-case assumptions are utilized to calculate the quality-of-transmission (QoT) with the provisioning of high-margin requirements. To this aim, precise and advanced estimation of the QoT of the LP is essential for reducing this provisioning margin. In this investigation, we present convolutional-neural-networks (CNN) based architecture to accurately calculate QoT before the actual deployment of LP in an unseen network. The proposed model is trained on the data acquired from already established LP of a completely different network. The metric considered to evaluate the QoT of LP is the generalized signal-to-noise ratio (GSNR). The synthetic dataset is generated by utilizing well appraised GNPy simulation tool. Promising results are achieved, showing that the proposed CNN model considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin.
Convolutional neural network for quality of transmission prediction of unestablished lightpaths / Usmani, Fehmida; Khan, Ihtesham; Masood, Muhammad Umar; Ahmad, Arsalan; Shahzad, Muhammad; Curri, Vittorio. - In: MICROWAVE AND OPTICAL TECHNOLOGY LETTERS. - ISSN 0895-2477. - ELETTRONICO. - (2021). [10.1002/mop.32996]
| Titolo: | Convolutional neural network for quality of transmission prediction of unestablished lightpaths | |
| Autori: | ||
| Data di pubblicazione: | 2021 | |
| Rivista: | ||
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1002/mop.32996 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| mop.32996.pdf | 2a Post-print versione editoriale / Version of Record |  | Visibile a tuttiVisualizza/Apri | |
| J_Convolutional_Neural_Network_for_Quality_of_Transmission_Prediction_of_Unestablished_Lightpaths_04042021__Copy_ (2).pdf | 2. Post-print / Author's Accepted Manuscript | | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2915674
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