Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities

TL;DR

This paper introduces data-driven methods to predict the tail of latency distributions in wireless networks, aiming to ensure ultra-reliable low-latency communication crucial for cyber-physical systems.

Contribution

It applies mixture density networks and extreme value models to estimate rare latency outliers, enhancing reliability predictions in wireless networks.

Findings

Accurately predicts tail latency probabilities in WiFi and 5G networks.

Demonstrates improved reliability estimation over traditional methods.

Provides insights into network parameter sensitivities for tail latency.

Abstract

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.