Machine Learning Techniques Applied to System Characterization and Equalization

Zibar, Darko; Thrane, Jakob; Wass, Jesper; Jones, Rasmus T.; Piels, Molly; Schaeffer, Christian

doi:10.1364/ofc.2016.tu3k.1

Cited by 3 publications

(4 citation statements)

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“…Artificial neural networks are well suited machine learning tools to perform optical performance monitoring as they can be used to learn the complex mapping between samples or extracted features from the symbols and optical fiber channel parameters, such as OSNR, PMD, Polarization-dependent loss (PDL), baud rate and CD. The features that are fed into the neural network can be derived using different approaches relying on feature extraction from: 1) the power eye diagrams (e.g., Q-factor, closure, variance, root-meansquare jitter and crossing amplitude, as in [49]- [53], [69]); 2) the two-dimensional eye-diagram and phase portrait [54]; 3) asynchronous constellation diagrams (i.e., vector diagrams also including transitions between symbols [51]); and 4) histograms of the asynchronously sampled signal amplitudes [52], [53]. The advantage of manually providing the features to the algorithm is that the NN can be relatively simple, e.g., consisting of one hidden layer and up to 10 hidden units and does not require large amount of data to be trained.…”

Section: E Optical Performance Monitoringmentioning

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

Self Cite

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: E Optical Performance Monitoringmentioning

confidence: 99%

“…However, increasing the optical signal power beyond a certain value will enhance optical fiber nonlinearities which leads to Nonlinear Interference (NLI) noise. NLI will impact symbol detection and the focus of many papers, such as [31], [32], [69]- [73] has been on applying ML approaches to perform optimum symbol detection.…”

Section: Nonlinearity Mitigationmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

Self Cite

485

226

View full text Add to dashboard Cite

show abstract

“…The most widely used ML tools for OPM are ANNs, which can be fed either with the statistical features of monitored data, or directly with the raw monitored data. Examples of features are Q-factor, closure, variance, root-meansquare jitter and crossing amplitude, extracted from power eye diagrams [39]- [42], [58], [59] and phase portraits [45], asynchronous constellation diagrams including transitions between symbols [39], or histograms of the asynchronously sampled signal amplitudes [41], [42]. When directly fed with raw monitored data, ANNs require complex architectures with a high number of neurons and hidden layers and a massive amount of training data to enable automatic extraction of signal quality indicators, such as PMD, PDL, CD, etc.…”

Section: A Optical Performance Monitoring (Opm)mentioning

confidence: 99%

A Tutorial on Machine Learning for Failure Management in Optical Networks

Musumeci

Rottondi

Corani

et al. 2019

J. Lightwave Technol.

103

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Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine Learning (ML) promises to revolutionize the (mostly manual and humandriven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task.

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“…Several machine learning algorithms have been proposed to combat fiber nonlinearity, such as support vector machine, K-nearest neighbours, and supervised k-means clustering [9][10][11][12]. The principle of these techniques consists of creating nonlinear decision boundaries by taking into account the nonlinear distortions.…”

Section: Introductionmentioning

confidence: 99%

Fiber Nonlinearity Mitigation via the Parzen Window Classifier for Dispersion Managed and Unmanaged Links

Amari

Lin

Dobre

et al. 2019

2019 21st International Conference on Transparent Optical Networks (ICTON)

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Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects. In this work, a machine learning-based classification technique, known as the Parzen window (PW) classifier, is applied to mitigate the nonlinear effects in the optical channel. The PW classifier is used as a detector with improved nonlinear decision boundaries more adapted to the nonlinear fiber channel. Performance improvement is observed when applying the PW in the context of dispersion managed and dispersion unmanaged systems.

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Machine Learning Techniques Applied to System Characterization and Equalization

Cited by 3 publications

References 0 publications

An Overview on Application of Machine Learning Techniques in Optical Networks

An Overview on Application of Machine Learning Techniques in Optical Networks

A Tutorial on Machine Learning for Failure Management in Optical Networks

Fiber Nonlinearity Mitigation via the Parzen Window Classifier for Dispersion Managed and Unmanaged Links

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