Dynamic mitigation of EDFA power excursions with machine learning

Huang, Yishen; Gutterman, Craig; Samadi, Payman; Cho, Patricia B.; Samoud, Wiem; Ware, Cédric; Lourdiane, Mounia; Zussman, Gil; Bergman, Keren

doi:10.1364/oe.25.002245

Cited by 38 publications

(19 citation statements)

References 15 publications

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“…Differently from the previous references, in [61] the issue of modelling the channel dependence of EDFA power excursion is approached by defining a regression problem, where the input feature set is an array of binary values indicating the occupation of each spectrum channel in a WDM grid and the predicted variable is the post-EDFA power discrepancy. Two learning approaches (i.e., the Ridge regression and Kernelized Bayesian regression models) are compared for a setup with 2 and 3 amplifier spans, in case of single-channel and superchannel add-drops.…”

Section: B Optical Amplifiers Controlmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

483

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: B Optical Amplifiers Controlmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

483

226

View full text Add to dashboard Cite

show abstract

“…non-Gaussian symmetric noise, laser phase noise and nonlinear phase noise) in zero-dispersion and dispersion managed links [12]. Ridge and kernelized Bayesian regression models have been employed for the characterization and mitigation of power excursions in gain-controlled erbium doped fiber amplifiers (EDFAs) [13].…”

Section: Related Workmentioning

confidence: 99%

Machine-Learning Method for Quality of Transmission Prediction of Unestablished Lightpaths

Rottondi¹,

Barletta

Giusti³

et al. 2018

J. Opt. Commun. Netw.

178

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Predicting the Quality of Transmission (QoT) of a lightpath prior to its deployment is a step of capital importance for an optimized design of optical networks. Due to the continuous advances in optical transmission, the number of design parameters available to system engineers (say, e.g., modulation formats, baud rate, code rate, etc.) is growing dramatically, thus significantly increasing the alternative scenarios for lightpath deployment. As of today, existing (pre-deployment) estimation techniques for lightpath QoT belong to two categories: "exact" analytical models estimating physical layer impairments, which provide accurate results but incur heavy computational requirements, and margined formulas which are computationally faster, but typically introduce high link margins that lead to underutilization of network resources. In this paper we explore a third option, i.e., Machine Learning (ML), as ML techniques have been already successfully applied for optimization and performance prediction of complex systems where analytical models are hard to derive and/or numerical procedures impose high computational burden. We investigate a ML classifier that predicts whether the bit-error rate of unestablished lightpaths meets the required system threshold, based on traffic volume, desired route and modulation format. The classifier is trained and tested on synthetic data and its performance is assessed over different network topologies and for various combinations of classification features. Results in terms of classifier accuracy are promising and motivate further investigation over real field data.

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“…In case label is a real number, then this task is termed as regression. If the label is in the from of the limited number of un-ordered values, then this is called as classification [2].…”

Section: Find the Available Algorithmsmentioning

confidence: 99%

Machine learning Techniques for Hotel Online Reputation

Chaudhary¹,

Aeron²,

Vijay³

2019

IJITEE

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Now days when someone decide to book a hotel, previous online reviews of the hotels play a major role in determining the best hotel within the budget of the customer. Previous Online reviews are the most important motivation for the information that are used to analyse public opinion. Because of the high impact of the reviews on business, hotel owners are always highly concerned and focused about the customer feedback and past online reviews. But all reviews are not true and trustworthy, sometime few people may intentionally generate the fake reviews to make some hotel famous of to defame. Therefore it is essential to develop and propose the techniques for analysis of reviews. With the help of various machine learning techniques viz. Supervised machine learning technique, Text mining, Unsupervised machine learning technique, Semi-supervised learning, Reinforcement learning etc we may detect the fake reviews. This paper gives some notions of using machine learning techniques in analysis of past online reviews of hotels, Based on the observation it also suggest the optimal machine learning technique for a particular situation

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Dynamic mitigation of EDFA power excursions with machine learning

Cited by 38 publications

References 15 publications

An Overview on Application of Machine Learning Techniques in Optical Networks

An Overview on Application of Machine Learning Techniques in Optical Networks

Machine-Learning Method for Quality of Transmission Prediction of Unestablished Lightpaths

Machine learning Techniques for Hotel Online Reputation

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