IEEE 802.1 Time-sensitive Networking (TSN) protocols have recently been
proposed to replace legacy networking technologies across different
mission-critical systems (MCSs). Design, configuration, and maintenance of TSN
within MCSs require advanced methods to tackle the highly complex and
interconnected nature of those systems. Accordingly, artificial intelligence
(AI) and machine learning (ML) models are the most prominent enablers to
develop such methods. However, they usually require a significant amount of
data for model training, which is not easily accessible. This short paper aims
to recapitulate the need for TSN datasets to flourish research on AI/ML-based
techniques for TSN systems. Moreover, it analyzes the main requirements and
alternative designs to build a TSN platform to synthesize realistic datasets.

通过人工智能和机器学习模型来研发时间敏感网络系统的方法，需要大量有效的 Time-sensitive Networking 数据集。本文总结了对于以人工智能和机器学习为基础的时间敏感网络系统技术研究，演练制作仿真真实数据集的主要需求和备选设计。

面向 IEEE 802.1 时态网络的数据集合成

Towards Synthesizing Datasets for IEEE 802.1 Time-sensitive Networking

With the emergence of new application areas, such as cyber-physical systems
and human-in-the-loop applications, there is a need to guarantee a certain
level of end-to-end network latency with extremely high reliability, e.g.,
99.999%. While mechanisms specified under IEEE 802.1as time-sensitive
networking (TSN) can be used to achieve these requirements for switched
Ethernet networks, implementing TSN mechanisms in wireless networks is
challenging due to their stochastic nature. To conform the wireless link to a
reliability level of 99.999%, the behavior of extremely rare outliers in the
latency probability distribution, or the tail of the distribution, must be
analyzed and controlled. This work proposes predicting the tail of the latency
distribution using state-of-the-art data-driven approaches, such as mixture
density networks (MDN) and extreme value mixture models, to estimate the
likelihood of rare latencies conditioned on the network parameters, which can
be used to make more informed decisions in wireless transmission. Actual
latency measurements of IEEE 802.11g (WiFi), commercial private and a
software-defined 5G network are used to benchmark the proposed approaches and
evaluate their sensitivities concerning the tail probabilities.

使用数据驱动的方法，预测网络参数条件下稀有延迟的概率分布尾部，以实现对无线传输中的网络延迟的更精确控制。