Marginless Operation of Optical Networks

Delezoide, Camille; Christodoulopoulos, Kostas; Kretsis, Aristotelis; Argyris, N.; Kanakis, Giannis; Sgambelluri, Andrea; Sambo, N.; Giardina, Pietro G.; Bernini, Giacomo; Roccato, D.; Percelsi, Alessandro; Morro, Roberto; Avramopoulos, H.; Varvarigos, Emmanouel; Castoldi, P.; Layec, Patricia; Bigo, S.

doi:10.1109/jlt.2018.2881840

Cited by 32 publications

(12 citation statements)

References 12 publications

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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%

“…The requirement of the QoT estimations differs in different scenarios. Some need to judge whether one light path can be established or not [4,21,38], and some need the specific value of the QoT metrics. For the former, ML classification methods [43] can be used such as K-nearest neighbors (KNN), random forests (RF), support vector machine (SVM), logistic regression (LR), ANN and so forth.…”

Section: Ai-based Qot Modelingmentioning

confidence: 99%

“…The result also shows that a bigger dataset can help to reach a higher accuracy. In [21], the generalized optical signal-to-noise ratio (gOSNR), baud rate, modulation format, FEC, slot-size and so on are used to estimate the BER and the training data is obtained from a practical system. Therefore, this model can enable controllers to find the optimum configuration of a light path for each specific network.…”

Section: Ai-based Qot Modelingmentioning

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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Section: Background and Challengesmentioning

confidence: 99%

Section: Ai-based Qot Modelingmentioning

confidence: 99%

Section: Ai-based Qot Modelingmentioning

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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“…This problem can be easily resolved by using a more accurate and flexible model based on ML techniques. In [37], the BER of the link is continuously monitored. The relationship between the link configurations, such as baud rate, forward error correction (FEC) overhead, etc., and the BER is learned by the stochastic gradient descent polynomial regression, which can then be used to choose an optimal link setting according to the real status of the optical network.…”

Section: The Reduction Of System Marginsmentioning

confidence: 99%

An overview of ML-based applications for next generation optical networks

Gao

Liu

et al. 2020

Sci. China Inf. Sci.

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Over the past few decades, the demand for the capacity and reliability of optical networks has continued to grow. In the meantime, optical networks with larger knowledge scales have become sources of numerous heterogeneous data. In order to handle these new challenges, many issues need to be resolved, among which the low-margin optical networks design, power optimization, routing and wavelength assignment (RWA), failure management are quite important. However, the use of traditional algorithms in the above four applications shows some shortcomings. Fortunately, artificial intelligence (AI), especially machine learning (ML), is regarded as one of the most promising methods to overcome these shortcomings. In this study, we review the applications of ML methods in solving these four issues. Although many ML-based researches have emerged, the applications of ML techniques in optical networks still face challenges. Therefore, we also discuss some possible future directions of investigating ML-based approaches in optical networks.

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“…It can provide network operators with information about spectrum reserves that operators can use opportunistically. They may even be able to lease their precious spectrum resources to other operators while still preserving their data transmission requirements [15,16].…”

Section: Introductionmentioning

confidence: 99%

Cross-layer static resource provisioning for dynamic traffic in flexible grid optical networks

Agrell

Brandt-Pearce

2021

J. Opt. Commun. Netw.

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Flexible grid networks need rigorous resource planning to avoid network over-dimensioning and resource over-provisioning. The network must provision the hardware and spectrum resources statically, even for dynamic random bandwidth demands, due to the infrastructure of flexible grid networks, hardware limitations, and the reconfiguration speed of the control plane. We propose a flexible-online-offline probabilistic (FOOP) algorithm for the static spectrum assignment of random bandwidth demands. The FOOP algorithm considers the probabilistic nature of random bandwidth demands and balances hardware and control plane pressures with spectrum assignment efficiency. The FOOP algorithm uses the probabilistic spectrum Gaussian noise (PSGN) model to estimate the physical-layer impairment (PLIs) for random bandwidth traffic. Compared to a benchmark spectrum assignment algorithm and a widely applied PLI estimation model, the proposed FOOP algorithm using the PSGN model saves up to 49% of network resources.

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Marginless Operation of Optical Networks

Cited by 32 publications

References 12 publications

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

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

An overview of ML-based applications for next generation optical networks

Cross-layer static resource provisioning for dynamic traffic in flexible grid optical networks

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