Optical Performance Monitoring Using Artificial Neural Networks Trained With Empirical Moments of Asynchronously Sampled Signal Amplitudes

Khan, Faisal Nadeem; Shen, Thomas Shun Rong; Zhou, Yudi; Lau, Alan Pak Tao; Lu, Chao

doi:10.1109/lpt.2012.2190762

Cited by 59 publications

(34 citation statements)

References 7 publications

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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%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

468

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%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

468

226

View full text Add to dashboard Cite

show abstract

“…It may be difficult for analytical models to achieve these two goals simultaneously but ML-aided methods can help to fulfill these requirements. For instance, samples of received signals can be input to ML algorithms for monitoring the chromatic dispersion (CD), polarization-mode dispersion (PMD) and optical signal-to-noise ratio (OSNR) at the same time [14]. Moreover, when obtaining information from the receiver digital signal processing (DSP) modules, ML methods may be able to monitor the QoT or impairments without any external devices such as the optical spectrum analyzer (OSA) [15].…”

Section: Monitoringmentioning

confidence: 99%

“…In [14], an ANN is used to monitor the OSNR, CD and PMD simultaneously with empirical asynchronously sampled signal amplitudes. In [57], to make an easier monitoring procedure without labor-intensive feature engineering, deep neural networks (DNN) are used to monitor the OSNR with asynchronously sampled raw data.…”

Section: Ai-based Qot and Impairment Monitoringmentioning

confidence: 99%

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

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With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

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“…By extracting the features from asynchronous amplitude histograms (AAHs) and asynchronous delay-tap plots (ADTPs), the popular pattern recognition algorithms are used for SRI [2][3][4][5], such as artificial neutral network (ANN). But these methods can only identify the symbol rate of a received signal when it has been considered during the training process.…”

Section: Introductionmentioning

confidence: 99%