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 (AEs) and deep neural networks. 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 modeling and length prediction and a noticeable performance of the AEs for unsupervised IQ constellation feature extraction and relevance analysis.

© 2022 Optica Publishing Group

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