The next generation of network capabilities coupled with artificial intelligence (AI) can provide innovative solutions for network control and self-optimisation. Network control demands a detailed knowledge of the network components to enforce the correct control rules. To this end, an immense number of metrics related to devices, flows, network rules, etc. can be used to describe the state of the network and to gain insights about which rule to enforce depending on the context. However, selection of the most relevant metrics often proves challenging and there is no readily available tool that can facilitate the dataset extraction and labelling for AI model training. This research work therefore first develops an analysis of the most relevant metrics in terms of network control to create a training dataset for future AI development purposes. It then presents a new architecture to allow the extraction of these metrics from a 5G network with a novel dataset visualisation and labelling tool to help perform the exploratory analysis and the labelling process of the resultant dataset. It is expected that the proposed architecture and its associated tools would significantly speed up the training process, which is crucial for the data-driven approach in developing AI-based network control capabilities.

中文翻译：

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NetLabeller：具有数据提取和标签框架的架构，适用于 5G 以外的网络

下一代网络能力与人工智能（AI）相结合，可以为网络控制和自我优化提供创新的解决方案。网络控制需要对网络组件有详细的了解，以执行正确的控制规则。为此，可以使用与设备、流量、网络规则等相关的大量指标来描述网络状态，并了解根据上下文执行哪个规则。然而，选择最相关的指标通常具有挑战性，并且没有现成的工具可以促进人工智能模型训练的数据集提取和标记。因此，这项研究工作首先对网络控制方面最相关的指标进行分析，以创建用于未来人工智能开发目的的训练数据集。然后，它提出了一种新的架构，允许使用新颖的数据集可视化和标记工具从 5G 网络中提取这些指标，以帮助对结果数据集执行探索性分析和标记过程。预计所提出的架构及其相关工具将显着加快训练过程，这对于开发基于人工智能的网络控制能力的数据驱动方法至关重要。