Novel Evolutionary Planning Technique for Flexible-grid Transmission in Optical Networks

Matheus R. Sena; Pedro J. Freire; Leonardo D. Coelho; Alex F. Santos; Antonio Napoli; Raul C. Almeida

doi:10.1016/j.osn.2021.100648

Novel Evolutionary Planning Technique for Flexible-grid Transmission in Optical Networks

Matheus R. Sena^*, Pedro J. Freire, Leonardo D. Coelho, Alex F. Santos, Antonio Napoli, Raul C. Almeida

^*Corresponding author for this work

Aston Institute of Photonics Technology (AIPT)

Research output: Contribution to journal › Article › peer-review

Abstract

This paper proposes a novel joint resource allocation technique for flexible-grid systems by utilizing non-dominant sort genetic algorithm (NSGA-II) in a multi-objective optimization framework. It pioneers the implementation of an evolutionary mechanism to optimize resources as means of mitigation of physical layer impairments. This investigation initially introduces a proposal in which bandwidth reduction, maximization of the minimum signal-to-noise ratio (SNR) margin, and minimization/maximization of the sum of SNR margins are studied under dual-objective Pareto analysis in the link-level scenario. Later, the technique extends existing provisioning strategies for network planning by targeting the reduction of blocking and spectral utilization of optical connections.

Original language	English
Article number	100648
Journal	Optical Switching and Networking
Volume	43
Early online date	22 Sep 2021
DOIs	https://doi.org/10.1016/j.osn.2021.100648
Publication status	Published - Feb 2022

Bibliographical note

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Funding: This work has received partial funding from the EU Horizon 2020 program under the Marie Skodowska-Curie grant agreement No. 813144 (REAL-NET) and the Brazilian National Council for Scientific and Technological Development (CNPq), as well as the institutional support from UFPE and UFBA.

Keywords

Elastic optical networks
GN-model
Genetic algorithms
Network planning
Physical layer impairment

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10.1016/j.osn.2021.100648

Novel Evolutionary Planning Technique
© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Accepted author manuscript, 1.65 MBLicence: CC BY-NC-ND 3.0

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title = "Novel Evolutionary Planning Technique for Flexible-grid Transmission in Optical Networks",

abstract = "This paper proposes a novel joint resource allocation technique for flexible-grid systems by utilizing non-dominant sort genetic algorithm (NSGA-II) in a multi-objective optimization framework. It pioneers the implementation of an evolutionary mechanism to optimize resources as means of mitigation of physical layer impairments. This investigation initially introduces a proposal in which bandwidth reduction, maximization of the minimum signal-to-noise ratio (SNR) margin, and minimization/maximization of the sum of SNR margins are studied under dual-objective Pareto analysis in the link-level scenario. Later, the technique extends existing provisioning strategies for network planning by targeting the reduction of blocking and spectral utilization of optical connections.",

keywords = "Elastic optical networks, GN-model, Genetic algorithms, Network planning, Physical layer impairment",

author = "Sena, {Matheus R.} and Freire, {Pedro J.} and Coelho, {Leonardo D.} and Santos, {Alex F.} and Antonio Napoli and Almeida, {Raul C.}",

note = "{\textcopyright} 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Funding: This work has received partial funding from the EU Horizon 2020 program under the Marie Skodowska-Curie grant agreement No. 813144 (REAL-NET) and the Brazilian National Council for Scientific and Technological Development (CNPq), as well as the institutional support from UFPE and UFBA.",

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doi = "10.1016/j.osn.2021.100648",

language = "English",

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AU - Sena, Matheus R.

AU - Freire, Pedro J.

AU - Coelho, Leonardo D.

AU - Santos, Alex F.

AU - Napoli, Antonio

AU - Almeida, Raul C.

N1 - © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Funding: This work has received partial funding from the EU Horizon 2020 program under the Marie Skodowska-Curie grant agreement No. 813144 (REAL-NET) and the Brazilian National Council for Scientific and Technological Development (CNPq), as well as the institutional support from UFPE and UFBA.

PY - 2022/2

Y1 - 2022/2

N2 - This paper proposes a novel joint resource allocation technique for flexible-grid systems by utilizing non-dominant sort genetic algorithm (NSGA-II) in a multi-objective optimization framework. It pioneers the implementation of an evolutionary mechanism to optimize resources as means of mitigation of physical layer impairments. This investigation initially introduces a proposal in which bandwidth reduction, maximization of the minimum signal-to-noise ratio (SNR) margin, and minimization/maximization of the sum of SNR margins are studied under dual-objective Pareto analysis in the link-level scenario. Later, the technique extends existing provisioning strategies for network planning by targeting the reduction of blocking and spectral utilization of optical connections.

AB - This paper proposes a novel joint resource allocation technique for flexible-grid systems by utilizing non-dominant sort genetic algorithm (NSGA-II) in a multi-objective optimization framework. It pioneers the implementation of an evolutionary mechanism to optimize resources as means of mitigation of physical layer impairments. This investigation initially introduces a proposal in which bandwidth reduction, maximization of the minimum signal-to-noise ratio (SNR) margin, and minimization/maximization of the sum of SNR margins are studied under dual-objective Pareto analysis in the link-level scenario. Later, the technique extends existing provisioning strategies for network planning by targeting the reduction of blocking and spectral utilization of optical connections.

KW - Elastic optical networks

KW - GN-model

KW - Genetic algorithms

KW - Network planning

KW - Physical layer impairment

UR - https://www.sciencedirect.com/science/article/pii/S157342772100045X

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DO - 10.1016/j.osn.2021.100648

M3 - Article

VL - 43

M1 - 100648

ER -

Novel Evolutionary Planning Technique for Flexible-grid Transmission in Optical Networks

Abstract

Bibliographical note

Keywords

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