Survey on the Use of Machine Learning for Quality of Transmission Estimation in Optical Transport Networks

• Published in: Journal of lightwave technology Vol. 40; no. 17; pp. 5803 - 5815
• Main Authors: Ayassi, Reda, Triki, Ahmed, Crespi, Noel, Minerva, Roberto, Laye, Maxime
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-09-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers (IEEE)/Optical Society of America(OSA)
• Subjects: Algorithms; Analytical models; Computational modeling; Computer Science; Design engineering; Estimation; Machine learning; Mathematical models; Networking and Internet Architecture; Optical amplifiers; Optical communication; Optical fiber networks; optical networks; Optical noise; Performance evaluation; Provisioning; QoT; Signal to noise ratio; WDM
• ISSN: 0733-8724; 1558-2213
• DOI: 10.1109/JLT.2022.3184178

Estimating the Quality of Transmission (QoT) of the optical signal from source to destination nodes is the cornerstone of design engineering and service provisioning in optical transport networks. Recent studies have turned to Machine Learning (ML) techniques to improve the accuracy of QoT estimation. In this paper, we survey the literature on this topic and classify the studies into categories based on their scope. Accordingly, we distinguish four categories of ML-based solutions: i) check lightpath feasibility, ii) estimate a lightpath's QoT, iii) enhance existing analytical models and iv) improve model generalization. We describe the proposed solutions in each category in terms of ML algorithms, inputs/outputs of the models, source of data and performance evaluation. Deploying a ML-based solution in the real field is not straightforward and presents several challenges. Therefore, we also discuss from an operator's perspective the potential of these solutions for real-field deployment.