Machine learning-based algorithm for core allocation in spatial division multiplexing elastic optical networks - Lacerda Jr., J. C.; Sousa, C. E. B.; Morais, A. G.; Cartaxo, A. V. T.; Soares, A. C. B. (Optical Fiber Technology, 2025)
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.