QoS Predictability in V2X Communication with Machine Learning

Abstract

An important use case in fifth generation systems are vehicular applications, where, reliability and low latency are the main requirements. In order to determine if a vehicular application can be used one can apply machine learning (ML) tools to determine if these constraints are met, which open questions such as “which data is available”, “which features to use”, “the quality of this prediction”, etc. In this paper we address some aspects of predicting quality-of-service (QoS) in a cellular vehicular-to-everything scenario, where we employ supervised learning as well as the autoregressive integrated moving average filter to predict if a packet can be delivered within a desired latency window. Particularly, we are interested in the reliability of this prediction, including predicting if a packet generated some time ahead will be delivered in time. Such information is essential when asserting that a vehicular application can indeed be employed safely. We show via simulation results that ML can be used as a prediction tool in vehicular applications. For instance, QoS levels can be predicted two seconds ahead with 85 % reliability.