five

Dataset of Numerical and Analytical Nonlinearity Coefficients of Multimode Fibers

收藏
DataCite Commons2024-08-30 更新2025-04-16 收录
下载链接:
https://mediatum.ub.tum.de/1752664
下载链接
链接失效反馈
官方服务:
资源简介:
The dataset contains design parameters and nonlinear coefficients for sets of multimode optical fibers (MMFs), from single-mode fibers to multimode fibers with thousands of modes, for the purpose of long-haul optical communications. The nonlinearity coefficients belong to two types of fibers analyzed in the related paper [Carniello24]: 1) the MMFs which have been considered to compute scaling laws and bounds on the matrix of nonlinearity coefficients γκ, 2) a number of optimized and manufactured MMFs proposed in the literature. For both groups, mode effective areas and intermodal effective areas, the Manakov nonlinear coefficient κ for the strong coupling regime (and the matrix of nonlinearity coefficients κ for the weak coupling regime for graded-index fibers), and the nonlinearity coefficient γκ (and the matrix of nonlinear coefficients γκ for graded-index fibers) are provided. For the first group of fibers, core radius, refractive index difference, and numerical aperture are additionally given. The analytically derived values for the matrix of nonlinearity coefficients γκ for graded-index fibers are included. The script and functions for symbolic computation to calculate them are also provided. A thorough description is given in the following paper: [Carniello24] Carniello P., Ferreira F. M., Hanik N., "Closed-Form Expressions for Nonlinearity Coefficients in Multimode Fibers", submitted to the IEEE Journal on Selected Areas in Communications, 2024. The provided files are: 1) a folder named "Databases" containing .mat files about the fiber data; 2) the script "researchData.m" explaining the content of the folder Databases and generating figures of the paper; 3) the script "kAnalyticSymbolic.m" for the symbolic computations for retrieving the analytic results contained in Databases/kSc_kImgc_gifsAnalytic.mat. All needed functions are provided in the folder Library.
提供机构:
Technical University of Munich
创建时间:
2024-08-30
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作