Jurandir C. Lacerda Jr. , Carlos E.B. Sousa , Aline G. Morais , Adolfo V.T. Cartaxo , André C.B. Soares
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引用次数: 0
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.
期刊介绍：
Innovations in optical fiber technology are revolutionizing world communications. Newly developed fiber amplifiers allow for direct transmission of high-speed signals over transcontinental distances without the need for electronic regeneration. Optical fibers find new applications in data processing. The impact of fiber materials, devices, and systems on communications in the coming decades will create an abundance of primary literature and the need for up-to-date reviews.
Optical Fiber Technology: Materials, Devices, and Systems is a new cutting-edge journal designed to fill a need in this rapidly evolving field for speedy publication of regular length papers. Both theoretical and experimental papers on fiber materials, devices, and system performance evaluation and measurements are eligible, with emphasis on practical applications.
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