Ultra-Wideband WDM Optical Network Optimization

Kozdrowski, Stanisław; Żotkiewicz, Mateusz; Sujecki, S.

doi:10.3390/photonics7010016

Cited by 23 publications

(7 citation statements)

References 23 publications

(7 reference statements)

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“…While it appears a common practice to use the leave-out-out procedure rather than k-fold cross-validation when working with small datasets, the only potential advantage of the former would be avoiding the pessimistic bias resulting from the fact that in each iteration of the latter 1 k of the data are not used for model creation. However, the leave-oneout procedure has high variance (that cannot be reduced by multiple repetitions since the procedure is fully deterministic) and excluding only a single instance from the training data may cause optimistic bias due to underrepresenting the differences between the training data and the test data.…”

Section: Model Evaluationmentioning

confidence: 99%

“…This particularly applies to Dense Wavelength Division Multiplexing (DWDM) networks whereby efficient use of network resources is of paramount importance. Introducing automation, frequent network reconfiguration, re-optimization and network reliability monitoring allows DWDM network operators to minimize the capital expenditures (Capex) and operating expenditures (Opex) [1][2][3][4][5][6]. Currently, software-defined networking (SDN) is used to achieve all these objectives.…”

Section: Introductionmentioning

confidence: 99%

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Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data

Cichosz

Kozdrowski

Sujecki

2021

Entropy

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Applying machine learning algorithms for assessing the transmission quality in optical networks is associated with substantial challenges. Datasets that could provide training instances tend to be small and heavily imbalanced. This requires applying imbalanced compensation techniques when using binary classification algorithms, but it also makes one-class classification, learning only from instances of the majority class, a noteworthy alternative. This work examines the utility of both these approaches using a real dataset from a Dense Wavelength Division Multiplexing network operator, gathered through the network control plane. The dataset is indeed of a very small size and contains very few examples of “bad” paths that do not deliver the required level of transmission quality. Two binary classification algorithms, random forest and extreme gradient boosting, are used in combination with two imbalance handling methods, instance weighting and synthetic minority class instance generation. Their predictive performance is compared with that of four one-class classification algorithms: One-class SVM, one-class naive Bayes classifier, isolation forest, and maximum entropy modeling. The one-class approach turns out to be clearly superior, particularly with respect to the level of classification precision, making it possible to obtain more practically useful models.

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Section: Model Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data

Cichosz

Kozdrowski

Sujecki

2021

Entropy

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“…In this section, the UW-WDM network optimization problem is formulated using MIP [10]. For this purpose the following sets are defined:…”

Section: Problem Formulationmentioning

confidence: 99%

“…However, the IP optimization results show that even if the IP approach is applicable, it is inefficient for larger networks since the design task is, in general, NP-complete [3]. The numerical efficiency limitations are particularly acute in the context of routing and wavelength assignment (RWA) [4][5][6] and routing and spectrum allocation (RSA) [7][8][9][10] problems, which are at the heart of DWDM network optimization. Moreover, the constraints and cost functions are in a general case nonlinear and thus may further complicate the situation.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design

et al. 2020

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The subject of the study was an application of nature-inspired metaheuristic algorithms to node configuration optimization in optical networks. The main objective of the optimization was to minimize capital expenditure, which includes the costs of optical node resources, such as transponders and amplifiers used in a new generation of optical networks. For this purpose a model that takes into account the physical phenomena in the optical network is proposed. Selected nature-inspired metaheuristic algorithms were implemented and compared with a reference, deterministic algorithm, based on linear integer programming. For the cases studied the obtained results show that there is a large advantage in using metaheuristic algorithms. In particular, the evolutionary algorithm, the bees algorithm and the harmony search algorithm showed superior performance for the considered data-sets corresponding to large optical networks; the integer programming-based algorithm failed to find an acceptable sub-optimal solution within the assumed maximum computational time. All optimization methods were compared for selected instances of realistic teletransmission networks of different dimensions subject to traffic demand sets extracted from real traffic data.

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“…Fiber laser systems and nonlinear optical converters are in demand for a wide variety of applied and fundamental problems, including telecommunications, noninvasive medical diagnostics, laser surgery, sensing, spectroscopy, etc. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Design and Dispersion Control of Microstructured Multicore Tellurite Glass Fibers with In-Phase and Out-of-Phase Supermodes

Anashkina

Andrianov

2021

Photonics

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High nonlinearity and transparency in the 1–5 μm spectral range make tellurite glass fibers highly interesting for the development of nonlinear optical devices. For nonlinear optical fibers, group velocity dispersion that can be controlled by microstructuring is also of great importance. In this work, we present a comprehensive numerical analysis of dispersion and nonlinear properties of microstructured two-, four-, six-, and eight-core tellurite glass fibers for in-phase and out-of-phase supermodes and compare them with the results for one-core fibers in the near- and mid-infrared ranges. Out-of-phase supermodes in tellurite multicore fibers are studied for the first time, to the best of our knowledge. The dispersion curves for in-phase and out-of-phase supermodes are shifted from the dispersion curve for one-core fiber in opposite directions; the effect is stronger for large coupling between the fields in individual cores. The zero dispersion wavelengths of in-phase and out-of-phase supermodes shift to opposite sides with respect to the zero-dispersion wavelength of a one-core fiber. For out-of-phase supermodes, the dispersion can be anomalous even at 1.55 μm, corresponding to the operating wavelength of Er-doped fiber lasers.

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Ultra-Wideband WDM Optical Network Optimization

Cited by 23 publications

References 23 publications

Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data

Learning to Classify DWDM Optical Channels from Tiny and Imbalanced Data

A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design

Design and Dispersion Control of Microstructured Multicore Tellurite Glass Fibers with In-Phase and Out-of-Phase Supermodes

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