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.

翻译：随着网络物理系统与人机协同应用等新兴领域的出现，需要以极高可靠性（例如99.999%）保障端到端网络延迟的特定水平。虽然IEEE 802.1as时间敏感网络（TSN）规范下的机制可用于在交换式以太网中实现这些要求，但由于无线网络的随机特性，在其中实施TSN机制面临挑战。为使无线链路达到99.999%的可靠性水平，必须分析并控制延迟概率分布中极端罕见异常值（即分布尾部）的行为。本文提出利用混合密度网络（MDN）和极值混合模型等前沿数据驱动方法预测延迟分布尾部，以估计网络参数条件下罕见延迟发生的可能性，这些信息可用于在无线传输中做出更明智的决策。通过IEEE 802.11g（WiFi）、商用私有网络和软件定义5G网络的实际延迟测量数据，对所提方法进行基准测试，并评估其在尾部概率方面的敏感性。