Machine Learning-Enabled Data Rate Prediction for 5G NSA Vehicle-to-Cloud Communications

Abstract

In order to satisfy the ever-growing Quality of Service (QoS) requirements of innovative services, cellular communication networks are constantly evolving. Recently, the 5G NonStandalone (NSA) mode has been deployed as an intermediate
strategy to deliver high-speed connectivity to early adopters of 5G
by incorporating Long Term Evolution (LTE) network infrastructure. In addition to the technological advancements, novel communication paradigms such as anticipatory mobile networking
aim to achieve a more intelligent usage of the available network
resources through exploitation of context knowledge. For this
purpose, novel methods for proactive prediction of the end-to-end
behavior are seen as key enablers. In this paper, we present a first
empirical analysis of client-based end-to-end data rate prediction
for 5G NSA vehicle-to-cloud communications. Although this
operation mode is characterized by massive fluctuations of the
observed data rate, the results show that conventional machine
learning methods can utilize locally acquirable measurements
for achieving comparably accurate estimations of the end-to-end
behavior.