Performance Analysis of a Data-Driven Quality-of-Transmission Decision Approach on a Dynamic Multicast-Capable Metro Optical Network

Panayiotou, Tania; Chatzis, Sotirios; Ellinas, Georgios

doi:10.1364/jocn.9.000098

Cited by 50 publications

(28 citation statements)

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“…This, however, results in inefficient utilization of the spectrum resources, especially when the worst-case PLIs are highly overestimated [3]. To alleviate these problems, machine learning (ML) techniques have been recently applied for inferring, from the QoT data of previously established connections, QoT models that are not a function of the PLIs and are robust to network changes [2,4,5]. Among the ML methods applied (K-nearest neighbors, logistic regression, support vector machines, and neural networks (NNs)), NNs have shown to present better generalization and higher accuracy [4].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for QoT Estimation of Unseen Optical Network States

Panayiotou¹,

Savva²,

Shariati³

et al. 2019

Optical Fiber Communication Conference (OFC) 2019

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Section: Introductionmentioning

confidence: 99%

Machine Learning for QoT Estimation of Unseen Optical Network States

Panayiotou¹,

Savva²,

Shariati³

et al. 2019

Optical Fiber Communication Conference (OFC) 2019

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“…Similarly, in the context of multicast transmission in optical network, a NN is trained in [43], [44], [46], [47] using as features the lightpath total length, the number of traversed EDFAs, the maximum link length, the degree of destination node and the channel wavelength used for transmission of candidate lightpaths, to predict whether the Q-factor will exceed a given system threshold. The NN is trained online with data mini-batches, according to the network evolution, to allow for sequential updates of the prediction model.…”

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

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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“…QoT modeling for an unestablished lightpath can help planning tools in the control plane to develop proper strategies of routing, wavelength assignment and signal configurations [20][21][22][23][24][25]. In EON, during the phase of network planning, the accuracy of QoT and impairment models is influenced by various configurable parameters like modulation format, symbol rate and physical path in optical networks.…”

Section: Background and Challengesmentioning

confidence: 99%

“…Firstly, ML methods are mostly data-driven [32], which means they enable the model to learn the characteristics of the dataset, in principle even without any theoretical information [4,[33][34][35][36]. This specific ability of learning adaptively with data allows ML models to be easily extended to any scenarios if the simulation, experiment or field-trial data for this situation can be obtained [13,23,37]. Secondly, for most optical networks, the number of tunable parameters for link configurations is limited.…”

mentioning

confidence: 99%

“…Secondly, for most optical networks, the number of tunable parameters for link configurations is limited. Therefore, the number of input parameters for QoT or impairment models are relatively small [5,33,38], which enables ML models to reach a good performance with simple structures such as the ANN with a small number of nodes and hidden layers [23]. In this case, these low-complexity ML models can calculate faster compared with some traditional models.…”

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confidence: 99%

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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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Performance Analysis of a Data-Driven Quality-of-Transmission Decision Approach on a Dynamic Multicast-Capable Metro Optical Network

Cited by 50 publications

References 10 publications

Machine Learning for QoT Estimation of Unseen Optical Network States

Machine Learning for QoT Estimation of Unseen Optical Network States

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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