An Adaptive Guard Band Selection based on Convolutional Neural Network

Routing, Modulation Level and Spectrum Assignment (RMLSA) are some of the main problems studied in elastic optical networks. This work focuses on the study of guard band selection, with one or more free slots between the circuits, which are used in the solutions of the RMLSA problem in order to redu...

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Bibliographic Details
Main Authors: Rodrigues, Wilson Leal, Sousa Monteiro, Neclyeux, Silva Borges, Fabbio Anderson, de Andrade Lira Rabelo, Ricardo, Branco Soares, Andre Castelo
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Bandwidth
convolutional neural network
Convolutional neural networks
deep learning
elastic optical networks
Optical computing
Optical fiber networks
Proposals
RMLSA problem
Training
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Summary:Routing, Modulation Level and Spectrum Assignment (RMLSA) are some of the main problems studied in elastic optical networks. This work focuses on the study of guard band selection, with one or more free slots between the circuits, which are used in the solutions of the RMLSA problem in order to reduce the interference between adjacent circuits in the spectrum optical. In this context, a new approach, called ADVANCE, which uses a convolutional neural network to adaptive guard band selection is proposed. A proposal performance is compared to other adaptive proposals: AGBA, GBUN and UTOPIAN. The proposal achieves a reduction in the bandwidth blocking probability of at least 86.56% relative to AGBA, 84.60% relative to GBUN and 73.26% relative to UTOPIAN.
ISSN:2577-1655
DOI:10.1109/SMC42975.2020.9283197