Simulation data used in management CCL for RAN slice performance improvement by subslicing

Simulation data used in management CCL for RAN slice performance improvement by subslicing
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Data used in PhD thesis and publication 4:
Folder NNtrainingdata\ contains training data used to train NN to decide subslice merge, split or no change in MCCL.
Folder Results_init\ contains slice simulation results of initialization of slice configuration if used subslice split algorithms in MCCL.
Folder Results_runtime_paper4\ contains slice simulation results of runtime if used subslice split algorithms in MCCL. Results are used in publication 4 of thesis.
This work has received funding partly from the European Union's Horizon 2020 Research and Innovation Program under Grant 951867 '5G-ROUTES' and Grant 101058505 '5G-TIMBER'. This work in the project "ICT programme" was supported by the European Union through European Social Fund, and TAR16013 Center of Excellence 'EXCITE IT'.

用于通过子切片技术提升无线接入网（Radio Access Network, RAN）切片性能的管理CCL仿真数据
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本数据集应用于博士学位论文及第4项发表成果：
文件夹`NNtrainingdata`内包含用于训练神经网络（Neural Network, NN）的训练数据，以实现MCCL环境下子切片的合并、拆分或保持不变的决策。
文件夹`Results_init`内包含在MCCL中应用子切片拆分算法时，切片配置初始化阶段的切片仿真结果。
文件夹`Results_runtime_paper4`内包含在MCCL中应用子切片拆分算法时，运行阶段的切片仿真结果，相关结果已用于本论文的第4项发表成果。

本研究部分经费来自欧盟地平线2020研究与创新计划，资助项目编号分别为951867「5G-ROUTES」与101058505「5G-TIMBER」。本项目隶属于“ICT计划”，由欧盟通过欧洲社会基金提供支持，并获TAR16013卓越中心「EXCITE IT」资助。

搜集汇总

背景与挑战

背景概述

该数据集包含用于RAN切片性能改进的模拟数据，主要用于神经网络训练和切片配置算法的研究。数据集由Tallinn University of Technology的研究人员创建，并得到了欧盟Horizon 2020等项目的资助。