The need to increase mobility and remove cables in industrial environments is pushing 5G as a valuable communication system to connect traditional deterministic Ethernet-based devices. One alternative is the adoption of Time Sensitive Networking (TSN) standards over 5G Non-Public Networks (5G NPN) deployed in the company premises. This scenario presents several challenges, the most relevant being the configuration of the 5G part to provide latency, reliability and throughput balance suitable to ensure that all the TSN traffic can be delivered on time. Our research work addresses this problem from the perspective of automata learning. Our aim is to learn from the live network to build a smart controller that can dynamically predict and apply a suitable configuration of the 5G NPN to satisfy the requirements of the current TSN traffic. The article presents the main ideas of this novel approach.

中文翻译：

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面向时间敏感网络的 5G 非公共网络的零接触配置

在工业环境中增加移动性和移除电缆的需求正在推动 5G 作为一种有价值的通信系统来连接传统的确定性基于以太网的设备。一种替代方法是在公司场所部署的 5G 非公共网络 (5G NPN) 上采用时间敏感网络 (TSN) 标准。这种情况提出了几个挑战，最相关的是 5G 部分的配置，以提供延迟、可靠性和吞吐量平衡，以确保所有 TSN 流量都能按时交付。我们的研究工作从自动机学习的角度解决了这个问题。我们的目标是从现网学习，构建一个智能控制器，可以动态预测和应用合适的 5G NPN 配置，以满足当前 TSN 流量的要求。