Network Planning With Actual Margins

Soumplis, Polyzois; Christodoulopoulos, K.; Quagliotti, Marco; Pagano, A.; Varvarigos, Emmanouel

doi:10.1109/jlt.2017.2743461

Cited by 40 publications

(40 citation statements)

References 23 publications

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“…Differently of the others approaches based on intelligent systems [14], it is not necessary the training phase and the proposed scheme can be performed in near-real time. In addition, the power budget is determinate in the planning stage of the network and margins are included [2], [4] and the proposed scheme will act during the regular operation of the EON. For the proposed scheme it is considered that the lightpaths were previously established from the resource allocation algorithms associated with route, modulation, bandwidth and spectrum.…”

Section: Proposed Schemementioning

confidence: 99%

“…In addition, the quality of transmission (QoT) of each lightpath is evaluated previously to resources allocation purpose, as well as to obtain reliable optical connectivity [2], [3]. The best knowledge of the QoT is needed in the design and operation phases, owing to the margin has to be added in the network when the QoT is not well established [4]. The QoT prediction can utilizes different methodologies based on sophisticated analytical models,…”

Section: Introductionmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Rentao Gu . approximated formulas and optical performance monitors (OPMs) [4], [5]. The QoT estimation with OPMs distributed in the route or in the coherent receiver can be appropriated in term of precision and computational complexity (CC) when integrated in to the active control plane to provide the link conditions in real time [1], [3].…”

Section: Introductionmentioning

confidence: 99%

“…The power, routing, modulation level and spectrum assignment (PRMSA) problem is usually determinate in the planning stage of the network and margins are included considering the QoT inaccuracies, equipment ageing, inter-channel interference, as well as the uncertainties of the optical power dynamics [7], [8]. However, there are some investigations to the development of resource allocation algorithms based on OPMs with reduced margins, which have considered ageing and inter-channel interference to configurable transponders with launch powers [5], regenerator placement [4] and the optimization of the physical topology for power minimization [9]. These algorithms can be based on derivative-free optimization (DFO), constrained direct-search algorithms [5], and mixed integer linear programming (MILP) [9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Heuristic Chaotic Hurricane-Aided Efficient Power Assignment for Elastic Optical Network

Santos

Abrão

2020

IEEE Access

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In this paper, we propose a dynamical power allocation (PA) procedure for elastic optical networks (EONs) based on the evolutionary hurricane search optimization (HSO) algorithm with a chaotic logistic map diversification strategy with the purpose of improving the capability to escape from local optima, namely PA-CHSO. The aiming is the dynamical control of the transmitted optical powers according to the variations of each link state due to traffic fluctuations, channel impairments, as well as other channel-power coupling effects. Such realistic EON scenarios are affected mainly by the channel estimation inaccuracy, channel ageing and power fluctuations. The link state is based on the channel estimation and quality of transmission (QoT) parameters obtained from the optical performance monitors (OPMs). Numerical results have demonstrated the effectiveness of the PA-CHSO to dynamically mitigate the power penalty under real measurement conditions with uncertainties and noise, as well as when perturbations in the optical transmit powers are considered. INDEX TERMS Adaptive power control algorithm, optical networks, hurricane algorithm, chaotic map, elastic optical networks.

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Section: Proposed Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Heuristic Chaotic Hurricane-Aided Efficient Power Assignment for Elastic Optical Network

Santos

Abrão

2020

IEEE Access

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“…In [4]- [9], different machine learning methods have been developed to numerically show how the estimation of the Quality of Transmission (QoT) can be improved for new traffic demands by monitoring network parameters. We can either improve the existing QoT model by decreasing the uncertainty on the network parameters via a hybrid machinelearning approach [4], or build a new model using classical machine-learning approaches [5]- [9]. For example, the approach used in [7], [8] is based on a Random Forest classifier, method fed by a set of network features (i.e., number of links, total length, longest link length, traffic volume and modulation format) and the output is a binary variable indicating whether the bit-error-rate is lower than the system threshold.…”

Section: Introductionmentioning

confidence: 99%

Automated Fiber Type Identification in SDN-Enabled Optical Networks

Seve

Pesic

Delezoide

et al. 2019

J. Lightwave Technol.

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Network design margins are introduced by quality of transmission estimator inaccuracies. Some of those inaccuracies are due to uncertainty on the fiber type deployed in optical networks, and on the value of the chromatic dispersion of deployed fibers. We propose, in this paper, a method to identify all unknown fiber types (and estimate chromatic dispersion) in an optical network to reduce said uncertainties. We monitor, collect, centralize, and correlate chromatic dispersion values accumulated over each established light path, already measured by coherent receivers to autonomously identify fiber types without traffic interruption. Thanks to this method, we can identify the fiber type of all the 41-unknown links for a 28-node network built with four different fiber types and only 100 light paths. Our method is also capable of distinguishing two fiber types that only differ by their dispersion slope. We have also deployed and demonstrated this fiber type identification method at the ECOC 2018 demo session with real-time communication with our Lab from the demo floor. The SDN-enabled optical network is based on ONOS controller that periodically provides real-time chromatic dispersion values of active light paths needed for the autonomous fiber type identification.

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Dynamic Abstraction of Optical Networks with Machine Learning Technologies

Yan

Gao

Wang

et al. 2020

Optical Network Design and Modeling

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The emerging 5G network will bring a huge amount of network traffic with big variations to optical transport networks. Softwaredefined optical networks and network function virtualization contribute to the vision for future programmable, disaggregated, and dynamic optical networks. Future optical networks will be more dynamic in network functions and network services, with high-frequency network reconfigurations. Optical connections will last shorter than that of the static optical networks. It's straightforward that Programmable optical hardware will require a reduced link margin to improve the hardware utilization. To configure network dynamically, real-time network abstractions are required for both current links and available-for-deploy links. The former abstraction guarantees the established links not be interfered by the newly established link while the latter abstraction provides information for intelligent network planning. In this talk, we use machine-learning technologies to process the collected monitoring data in a field-trial testbed to abstract performances of multiple optical channels. Based on the abstract information, a new channel can be established with maximum performance and minimized interference on the current signals. We demonstrated the dynamic network abstraction over a 563.4-km field-trial testbed for 8 dynamic optical channels with 32 Gbaud Nyquist PM-16QAM signals. The work can be further extended to support complex optical networks.

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Network Planning With Actual Margins

Cited by 40 publications

References 23 publications

Heuristic Chaotic Hurricane-Aided Efficient Power Assignment for Elastic Optical Network

Heuristic Chaotic Hurricane-Aided Efficient Power Assignment for Elastic Optical Network

Automated Fiber Type Identification in SDN-Enabled Optical Networks

Dynamic Abstraction of Optical Networks with Machine Learning Technologies

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