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Journal ArticleDOI

Design of Low-Margin Optical Networks

TLDR
Techniques that the network designer can use in order to increase the capacity of optical networks, extend their life, and decrease deployment cost (CAPEX) or total cost of ownership over their life duration are reviewed.
Abstract
We review margins used in optical networks and review a formerly proposed margin taxonomy. For each category of margins, we review techniques that the network designer can use in order to increase the capacity of optical networks, extend their life, and decrease deployment cost (CAPEX) or total cost of ownership over their life duration. Green field (new network deployments) and brown field techniques (used after initial network deployment) are discussed. The technology needed to leverage the margins and achieve the aforementioned gains are also reviewed, along with the associated challenges.

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Citations
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Journal ArticleDOI

Machine-learning method for quality of transmission prediction of unestablished lightpaths

TL;DR: A ML classifier is investigated that predicts whether the bit error rate of unestablished lightpaths meets the required system threshold based on traffic volume, desired route, and modulation format.
Journal ArticleDOI

BER Degradation Detection and Failure Identification in Elastic Optical Networks

TL;DR: In this paper, the authors analyze several failure causes affecting the quality of optical connections and propose two different algorithms: one focused on detecting significant bit error rate (BER) changes in optical connections, named as BANDO, and the other focused on identifying the most probable failure pattern, called LUCIDA.
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Soft failure localization during commissioning testing and lightpath operation

TL;DR: Simulation results are presented, showing the effectiveness of the TISSUE algorithm in properly exploiting OTC information to assess BER performance of quadrature-phase-shift-keying-modulated signals, and the high accuracy of the FEELING algorithm to correctly detect soft failures as laser drift, filter shift, and tight filtering.
Journal ArticleDOI

Learning process for reducing uncertainties on network parameters and design margins

TL;DR: In this article, a machine learning algorithm was used to reduce the uncertainties on the input parameters of the QoT model, improving the accuracy of the SNR estimation with respect to new optical demands in a brownfield phase.
References
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Journal ArticleDOI

EGN model of non-linear fiber propagation

TL;DR: This paper analyzes in detail the GN-model errors and derives a complete set of formulas accounting for all single, cross, and multi-channel effects that constitute the enhanced GN- model (EGN-model), which is found to be very good when assessing detailed span-by-span NLI accumulation and excellent when estimating realistic system maximum reach.
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Machine Learning Techniques in Optical Communication

TL;DR: In this paper, a nonlinear state-space model for nonlinearity mitigation, carrier recovery, and nanoscale device characterization is proposed, which allows for tracking and compensation of the XPM induced impairments by employing approximate stochastic filtering methods such as extended Kalman or particle filtering.
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Inter-Channel Nonlinear Interference Noise in WDM Systems: Modeling and Mitigation

TL;DR: In this paper, the authors review the modeling of inter-channel nonlinear interference noise (NLIN) in fiber-optic communication systems, focusing on the accurate extraction of the NLIN variance, the dependence on modulation format, the role of nonlinear phase-noise, and the existence of temporal correlations.
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Unavailability analysis of long-haul networks

TL;DR: This paper analyses a number of long-haul network architectures from an unavailability point of view, finding that self-healing rings and dual fed systems offer the highest level of survivability, by eliminating service impacts caused by cable cuts and equipment failures.
Journal ArticleDOI

Rate-adaptable optics for next generation long-haul transport networks

TL;DR: By using link-length demands from an exemplary distance-diverse network, it is demonstrated that time-domain hybrid-QAM-enabled fine-grain rate-adaptable transponders can reduce network cost by more than 20 percent within a traditional, fixed-bandwidth, wavelength-division-multiplexed grid.
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