Learning Process for Reducing Uncertainties on Network Parameters and Design Margins

Seve, E.; Pesic, Jelena; Delezoide, Camille; Bigo, S.; Pointurier, Yvan

doi:10.1364/jocn.10.00a298

Cited by 99 publications

(74 citation statements)

References 17 publications

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“…Note that S1 includes all the 11 features, whereas S2 excludes the features characterizing the nearest neighbor lightpaths. However, if we focus on the AUC of "near to threshold" instances, results obtained in scenario S1 are slightly higher, which leads us to conclude that information on the closest neighbors does provide some insights for classification of the instances with BER close to T , as intuition would suggest 5 .…”

Section: Analysis Of Feature Relevancementioning

confidence: 92%

“…An alternative approach to QoT prediction relies on sensing the QoT of already deployed lightpaths by means of optical performance monitors (OPMs) [4] installed at the receiver side and on exploiting the knowledge extracted from field data to predict the QoT of unestablished lightpaths [5]. To this aim, different Machine Learning (ML) techniques have been recently investigated, e.g., network kriging [6], Case Based Reasoning [7], and neural networks [8], [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Rottondi¹,

Barletta

Giusti³

et al. 2018

J. Opt. Commun. Netw.

171

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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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Section: Analysis Of Feature Relevancementioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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

Rottondi¹,

Barletta

Giusti³

et al. 2018

J. Opt. Commun. Netw.

171

View full text Add to dashboard Cite

show abstract

“…A database-oriented approach is proposed also in [42] to reduce uncertainties on network parameters and design margins, where field data are collected by a software defined network controller and stored in a central repository. Then, a QTool is used to produce an estimate of the field-measured Signal-to-Noise Ratio (SNR) based on educated guesses on the (unknown) network parameters and such guesses are iteratively updated by means of a gradient descent algorithm, until the difference between the estimated and the field-measured SNR falls below a predefined threshold.…”

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

461

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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“…In this way, ML-PLM can reach better performances and makes the model suitable for a dynamic network. In [51], gradient decent is used to correct the deviations of the input parameters for the QoT estimators. This method takes advantage of back-propagation algorithms embedded in many neural networks, which successfully reduces the uncertainty of models.…”

Section: Ai-based Qot Modelingmentioning

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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Learning Process for Reducing Uncertainties on Network Parameters and Design Margins

Cited by 99 publications

References 17 publications

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

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

An Overview on Application of Machine Learning Techniques in Optical Networks

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

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