Supervised Machine Learning Techniques for Quality of Transmission Assessment in Optical Networks

Elastic optical networks provide flexible bandwidth allocation which helps to optimize overall bandwidth usage in a network. Though the study in this field is more than a decade long, some recent developments like inclusion of machine learning tools and new protection based approaches have emerged. In this paper, we review some of the recent developments in elastic optical networks using machine learning, different routing schemes and protection approaches. We have pointed out future research scope and challenges in this area of work.

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“…According to the paper [3] [7], ii) Error prediction [8] [9], ii) Traffic prediction [10] [11] Others. Evaluate or predict quality of service.…”

Section: Techniques Applied To Elastic Optical Networkmentioning

confidence: 99%

Recent Developments on Elastic Optical Networks: A brief Survey

Mukherjee

Choudhury

2022

American Journal of Electronics & Communication

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“…As a firststep towards the estimation of the QoT in Flex-Grid/SDM optical networks, we have proposed (e.g. [7]) the application of different supervised machine learning models to classify unestablished lightpaths into high or low QoT categories within impairment-aware 10 Gb/s OOK wavelength-routed optical networks. The considered ML models include methods like support vector machines (SVM), logistic regression, classification and regression trees (CART), and random forests (RF).…”

Section: Lightpath Qot Estimation With Ai Techniquesmentioning

confidence: 99%

Elastic Networks Thematic Network Results I: Planning and Control of Flex-Grid/SDM

Moreno-Muro

Rumipamba-Zambrano

Perelló

et al. 2018

2018 20th International Conference on Transparent Optical Networks (ICTON)

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This paper overviews the approach of the Elastic Networks research network to address different issues of planning and control of Flex-Grid/SDM optical networks. Firstly, we present the Net2Plan open-source planning tool capabilities to model Flex-Grid/SDM networks; secondly a PCE-based Transport-SDN controller for packet over flex-grid optical networks is described. Finally results on machine-learning-based QoT classification techniques useful in planning and control tasks are reported.

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“…Machine learning classifiers are promising predictors that meet high precision and realtime requirements. Furthermore, they can automatically predict the QoT of unestablished lightpaths [12][13][14][15][16][17][18] (LR), and support vector machine (SVM), were evaluated and compared using the three dataset cases.…”

Section: Introductionmentioning

confidence: 99%

A QoT prediction technique based on machine learning and NLSE for QoS and new lightpaths in optical communication networks

Chen

et al. 2021

Front. Optoelectron.

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In this paper, we proposed a quality of transmission (QoT) prediction technique for the quality of service (QoS) link setup based on machine learning classifiers, with synthetic data generated using the transmission equations instead of the Gaussian noise (GN) model. The proposed technique uses some link and signal characteristics as input features. The bit error rate (BER) of the signals was compared with the forward error correction threshold BER, and the comparison results were employed as labels. The transmission equations approach is a better alternative to the GN model (or other similar margin-based models) in the absence of real data (i.e., at the deployment stage of a network) or the case that real data are scarce (i.e., for enriching the dataset/reducing probing lightpaths); furthermore, the three classifiers trained using the data of the transmission equations are more reliable and practical than those trained using the data of the GN model. Meanwhile, we noted that the priority of the three classifiers should be support vector machine (SVM) > K nearest neighbor (KNN) > logistic regression (LR) as shown in the results obtained by the transmission equations, instead of SVM > LR > KNN as in the results of the GN model.Keywords optical networks, quality of transmission (QoT), quality of service (QoS), link establishment, physical performances, bit error rate (BER), machine learning

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Supervised Machine Learning Techniques for Quality of Transmission Assessment in Optical Networks

Cited by 13 publications

References 11 publications

Recent Developments on Elastic Optical Networks: A brief Survey

Recent Developments on Elastic Optical Networks: A brief Survey

Elastic Networks Thematic Network Results I: Planning and Control of Flex-Grid/SDM

A QoT prediction technique based on machine learning and NLSE for QoS and new lightpaths in optical communication networks

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