Data-Driven Latency Probability Prediction for Wireless Networks:
  Focusing on Tail Probabilities
Samie Mostafavi Gourav Prateek Sharma James Gross
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.