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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Lacerda Jr., J. C., Sousa, C. E. B. , Morais, A. G., Cartaxo, A. V. T. & Soares, A. C. B. (2025). Machine learning-based algorithm for core allocation in spatial division multiplexing elastic optical networks. Optical Fiber Technology. 91
Exportar Referência (IEEE) 
J. C. Júnior et al.,  "Machine learning-based algorithm for core allocation in spatial division multiplexing elastic optical networks", in Optical Fiber Technology, vol. 91, 2025
Exportar BibTeX 
@article{júnior2025_1770190800732,
	author = "Lacerda Jr., J. C. and Sousa, C. E. B.  and Morais, A. G. and Cartaxo, A. V. T. and Soares, A. C. B.",
	title = "Machine learning-based algorithm for core allocation in spatial division multiplexing elastic optical networks",
	journal = "Optical Fiber Technology",
	year = "2025",
	volume = "91",
	number = "",
	doi = "10.1016/j.yofte.2025.104155",
	url = "https://www.sciencedirect.com/journal/optical-fiber-technology"
}
Exportar RIS 
TY  - JOUR
TI  - Machine learning-based algorithm for core allocation in spatial division multiplexing elastic optical networks
T2  - Optical Fiber Technology
VL  - 91
AU  - Lacerda Jr., J. C.
AU  - Sousa, C. E. B. 
AU  - Morais, A. G.
AU  - Cartaxo, A. V. T.
AU  - Soares, A. C. B.
PY  - 2025
SN  - 1068-5200
DO  - 10.1016/j.yofte.2025.104155
UR  - https://www.sciencedirect.com/journal/optical-fiber-technology
AB  - Spatial division multiplexing elastic optical networks (SDM-EONs) using multicore fibers (MCF) are promising candidates for the future transport networks. In MCFs, a new dimension is added to the resource allocation problem: core allocation. In this paper, a machine learning-based algorithm for core selection (MaLAC) in SDM-EONs is proposed. Compared with other three solutions proposed in the literature and a scenario with a low crosstalk level, MaLAC achieves at least 25.35% gain in terms of request blocking probability (RBP) and at least 24.81% for bandwidth blocking probability (BBP). In a scenario with a high crosstalk level, MaLAC achieves at least 8.16% gain for RBP and at least 9.28% for BBP.
ER  -