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Publication Detailed Description
Machine learning-based algorithm for core allocation in spatial division multiplexing elastic optical networks
Journal Title
Optical Fiber Technology
Year (definitive publication)
2025
Language
English
Country
United States of America
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Abstract
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.
Acknowledgements
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Keywords
Optical networks,Multi-core fiber,Physical layer impairments,Artificial intelligence,Neural networks,Machine learning
Fields of Science and Technology Classification
- Physical Sciences - Natural Sciences
- Civil Engineering - Engineering and Technology
- Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
- Materials Engineering - Engineering and Technology
Contributions to the Sustainable Development Goals of the United Nations
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