Cognitive Tool for Estimating the QoT of New Lightpaths

Aladin, Sandra; Tremblay, Christine

doi:10.1364/ofc.2018.m3a.3

Cited by 31 publications

(27 citation statements)

References 4 publications

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“…In [45], a random forest classifier along with two other tools namely k-nearest neighbor and support vector machine are used. The authors in [45] use three of the above-mentioned classifiers to associate QoT labels with a large set of lightpaths Fig. 7: The classification framework adopted in [41].…”

Section: A Quality Of Transmission Estimationmentioning

confidence: 99%

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An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

485

226

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Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management.The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation.In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions.

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Section: A Quality Of Transmission Estimationmentioning

confidence: 99%

“…to develop a knowledge base and find out which is the best classifier. It turns out from the analysis in [45], that the support vector machine is better in performance than the other two but takes more computation time.…”

Section: A Quality Of Transmission Estimationmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

485

226

View full text Add to dashboard Cite

show abstract

“…The KB used for QoT estimation was built using the data generation tool based on the Gaussian noise model described in [2]. The tool allows for channel BER estimation as a function of the linear and nonlinear noise contributions, as well as signal and link characteristics.…”

Section: System Setup and Data Pre-processingmentioning

confidence: 99%

“…ML algorithms for optical networking applications have been explored in the last years [1]. ML-based methods such as K-nearest neighbours (K-NN), support vector machine (SVM) and random forest (RF) have been proposed for estimating the quality of transmission (QoT) of unestablished lightpaths [2]. A comparative study of ML-based lightpath classifiers realized with a synthetic knowledge base (KB) of 25,600 bit error rate (BER) instances generated using the Gaussian noise model has shown that SVM outperforms RF and K-NN in terms of class prediction's accuracy [2].…”

Section: Introductionmentioning

confidence: 99%

“…ML-based methods such as K-nearest neighbours (K-NN), support vector machine (SVM) and random forest (RF) have been proposed for estimating the quality of transmission (QoT) of unestablished lightpaths [2]. A comparative study of ML-based lightpath classifiers realized with a synthetic knowledge base (KB) of 25,600 bit error rate (BER) instances generated using the Gaussian noise model has shown that SVM outperforms RF and K-NN in terms of class prediction's accuracy [2]. A ML physical layer model (ML-PLM) trained with physical layer parameters and a ML model (ML-M) using monitoring metrics combined with analytically generated link-based features were found to achieve a very good accuracy with a relatively small training database [3].…”

Section: Introductionmentioning

confidence: 99%

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Quality of Transmission Estimation and Short-Term Performance Forecast of Lightpaths

Aladin

Tran

Allogba

et al. 2020

J. Lightwave Technol.

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With ever-increasing traffic, the need more dynamic, flexible and autonomous optical networks is more important than ever. The availability of performance monitoring data makes it possible to leverage machine learning (ML) for fast quality of transmission (QoT) estimation and performance prediction of lightpaths in complex optical networks. In this work, we explore classifiers based on support vector machine (SVM) and artificial neural network (ANN) for QoT estimation of unestablished lightpaths. Using a synthetic knowledge base (KB), the classification accuracy of the ANN and SVM models decreased from 99%, with a complete feature set, to 85.03% and 88.52%, respectively, with a reduced feature set. We also propose a Long Short-Term Memory (LSTM), an Encoder-Decoder LSTM and a Gated Recurrent Unit (GRU) models, trained with 13-month field performance data, for lightpath signal-to-noise (SNR) prediction over forecast horizons up to 4 days. Positive R 2 values combined with low ( 0.285 dB) root mean square error (RMSE) indicated that the GRU model achieved slightly better predictions than the naive method for forecast horizons ranging from 1 to 96 hours, whereas the LSTM performed better over 24 to 96-hour forecast horizons. The Encoder-Decoder LSTM model achieved the lowest R 2 and the highest RMSE values (0.296 dB). Additional input data will be needed to improve the prediction accuracy of the LSTM and GRU models trained with single lightpath data.

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Machine Learning Assisted Quality of Transmission Estimation and Planning with Reduced Margins

Christodoulopoulos¹,

Sartzetakis²,

Soumplis³

et al. 2020

Optical Network Design and Modeling

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In optical transport networks, the Quality of Transmission (QoT) using a physical layer model (PLM) is estimated before establishing new or reconfiguring established optical connections. Traditionally, high margins are added to account for the model's inaccuracy and the uncertainty in the current and evolving physical layer conditions, targeting uninterrupted operation for several years, until the end-of-life (EOL). Reducing the margins increases network efficiency but requires accurate QoT estimation. We present two machine learning (ML) assisted QoT estimators that leverage monitoring data of existing connections to understand the actual physical layer conditions and achieve high estimation accuracy. We then quantify the benefits of planning/upgrading a network over multiple periods with accurate QoT estimation as opposed to planning with EOL margins.

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Cognitive Tool for Estimating the QoT of New Lightpaths

Cited by 31 publications

References 4 publications

An Overview on Application of Machine Learning Techniques in Optical Networks

An Overview on Application of Machine Learning Techniques in Optical Networks

Quality of Transmission Estimation and Short-Term Performance Forecast of Lightpaths

Machine Learning Assisted Quality of Transmission Estimation and Planning with Reduced Margins

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