Deep learning-based real-time analysis of lightpath optical constellations [Invited]
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hdl:2117/367377
Tipus de documentArticle
Data publicació2022-06-01
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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Abstract
Optical network automation requires accurate physical layer models, not only for provisioning but also for real-time analysis. In particular, In-Phase (I) and Quadrature (Q) constellation analysis enables deep understanding of the characteristics of optical connections (lightpaths), e.g., their length. In this paper, we present methods for modeling lightpaths based on deep learning. Specifically, we propose using autoencoders (AE) and deep neural networks (DNN). Models are trained and composed in a sandbox domain with the information received from the network controller and sent to the node agent that uses them to compare the features extracted from the received signal and the expected features returned by the models. We investigate two different use cases for lightpath analysis focused on lightpath length and optical signal power. The results show a remarkable accuracy for the lightpath modelling and length prediction and a noticeable performance of the AEs for unsupervised IQ constellation features extraction and relevance analysis. © 2021 Optical Society of America
Descripció
© [2022 Optical Society of America]. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.
CitacióRuiz, M.; Sequeira, D.; Velasco, L. Deep learning-based real-time analysis of lightpath optical constellations [Invited]. "Journal of optical communications and networking", 1 Juny 2022, vol. 14, núm. 6, p. C70-C81.
ISSN1943-0620
Versió de l'editorhttps://doi.org/10.1364/JOCN.451315
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