QoT Estimation for Unestablished Lighpaths using Machine Learning

Barletta, Luca; Giusti, Alessandro; Rottondi, Cristina; Tornatore, Massimo

doi:10.1364/ofc.2017.th1j.1

Cited by 78 publications

(48 citation statements)

References 4 publications

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“…CBR + learning/forgetting [36]: experimental demonstration of the QoT estimator [35] in a WDM 80 Gb/s PDM-QPSK testbed. Random forests classifier [37]: predicts the probability that the BER of a candidate lightpath will not exceed a given threshold.…”

Section: Qot Estimationmentioning

confidence: 99%

“…Another proposal for QoT estimation is that of Barletta et al [37], who apply a machine learning-based classifier, specifically a random forest, to predict the probability that the BER of a candidate lightpath will not exceed a given threshold. Finally, Oda et al [38] present the concept of "living network", an optical network which keeps records of its path-level performance, which takes advantage of BER information monitoring and of a learning process (based on linear regression) in order to estimate the BER of each new service request.…”

Section: Quality Of Transmission (Qot) Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata

Miguel

Durán

et al. 2018

Optical Switching and Networking

331

174

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Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

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Section: Qot Estimationmentioning

confidence: 99%

Section: Quality Of Transmission (Qot) Estimationmentioning

confidence: 99%

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata

Miguel

Durán

et al. 2018

Optical Switching and Networking

331

174

View full text Add to dashboard Cite

show abstract

“…The model can also monitor the performance of existing wavelengths and proactively move and/or groom them to better paths as conditions evolve. The general methodology used here belongs to the category of "QoT (Quality of Transmission Estimation)" in Table I of [2] and is perhaps closest to [12]. However, while [12] (and most of the related references in [2]) uses synthetic data, we use real data from a large ISP's network.…”

Section: Introductionmentioning

confidence: 99%

“…The general methodology used here belongs to the category of "QoT (Quality of Transmission Estimation)" in Table I of [2] and is perhaps closest to [12]. However, while [12] (and most of the related references in [2]) uses synthetic data, we use real data from a large ISP's network. Also while the model in [12] predicts whether the optical performance of a new wavelength will be good or bad, we predict actual BER allowing use of different thresholds depending on what the new wavelength will be used for and allowing comparison of alternate wavelength paths.…”

Section: Introductionmentioning

confidence: 99%

“…However, while [12] (and most of the related references in [2]) uses synthetic data, we use real data from a large ISP's network. Also while the model in [12] predicts whether the optical performance of a new wavelength will be good or bad, we predict actual BER allowing use of different thresholds depending on what the new wavelength will be used for and allowing comparison of alternate wavelength paths. Figure 1 depicts the framework for self-optimizing an IP/Optical network in a closed loop manner where future traffic prediction from machine learning, real-time network and traffic measurements, and knowledge based feedback on traffic changes and failures will collectively drive a joint global optimization engine for both the packet and optical layers.…”

Section: Introductionmentioning

confidence: 99%

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Two Use Cases of Machine Learning for SDN-Enabled IP/Optical Networks: Traffic Matrix Prediction and Optical Path Performance Prediction [Invited]

Choudhury¹,

Lynch²,

Thakur³

et al. 2018

J. Opt. Commun. Netw.

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We describe two applications of machine learning in the context of IP (Internet Protocol) /Optical networks. The first one allows agile management of resources in a core IP/Optical network by using machine learning for short-term and longterm prediction of traffic flows. It also allows joint global optimization of IP and optical layers using colorless/directionless (CD) ROADMs (Reconfigurable Optical Add Drop Multiplexers). Multilayer coordination allows for significant cost savings, flexible new services to meet dynamic capacity needs, and improved robustness by being able to proactively adapt to new traffic patterns and network conditions. The second application is important as we migrate our networks to Open ROADM networks, to allow physical routing without the need for detailed knowledge of optical parameters. We discuss a proof-of-concept study, where detailed performance data for established wavelengths in an existing ROADM network is used for machine learning to predict the optical performance of each wavelength. Both applications can be efficiently implemented by using a SDN (Software Defined Network) controller.

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Machine learning‐based predictive modeling for failure management of optical spatial mode division multiplexing system

Kaur

Parasher²,

Srivastava

et al. 2022

Int J Communication

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The bit error rate (BER) in optical communication systems is often get degraded due to various factors like launch power, dispersion, modal noise, and so on. Finding the most optimal launch power for a signal to provide acceptable BER is usually difficult on an installed link. Therefore, in this paper, an attempt has been made to use machine learning-based linear regression technique for predicting the optimal signal quality for spatial division multiplexed (SDM)-based fiber optical transmission system for a fixed distance. This technique helps in predicting the optimal value of continuous launch power. Therefore, to demonstrate the given concept in this research work, the generic setup of an SDM optical long-haul system of 20 km in length has been designed and simulated in Optisystem-14.0. The light sources in our experiments are two spatial optical transmitters with an emission wavelength of around 1550 nm. Abiding to the first step for the regression analysis, that is the data preparation, normality and correlation checks were performed. After this, a linear regression model is developed which was validated through a summary report, coefficients and diagnostic plots. Furthermore, the accuracy of the model is improved by employing Cook's distance. These tests help in dealing with the influence points that hinders the prediction ability of the proposed model. The results show that the R-squared value is 0.9366, and value of adjusted R 2 comes out to be around 0.9344; we therefore came to infer that the model explains nearly 94% variations in the dependent variables. K E Y W O R D Sbit error rate (BER), launch power (LP), linear regression, optical transmission systems, spatial division multiplex (SDM)

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QoT Estimation for Unestablished Lighpaths using Machine Learning

Cited by 78 publications

References 4 publications

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Two Use Cases of Machine Learning for SDN-Enabled IP/Optical Networks: Traffic Matrix Prediction and Optical Path Performance Prediction [Invited]

Machine learning‐based predictive modeling for failure management of optical spatial mode division multiplexing system

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