QoS prediction in radio vehicular environments via prior user information
This work addresses reliable wireless communication for the automotive industry, but it is incremental as it extends prior art by supporting longer prediction horizons.
The paper tackles the problem of predicting quality of service (QoS) in radio vehicular environments to support automotive use cases like autonomous driving, using ML tree-ensemble methods with data from a cellular test network to achieve predictions in the range of minutes by incorporating prior vehicle information.
Reliable wireless communications play an important role in the automotive industry as it helps to enhance current use cases and enable new ones such as connected autonomous driving, platooning, cooperative maneuvering, teleoperated driving, and smart navigation. These and other use cases often rely on specific quality of service (QoS) levels for communication. Recently, the area of predictive quality of service (QoS) has received a great deal of attention as a key enabler to forecast communication quality well enough in advance. However, predicting QoS in a reliable manner is a notoriously difficult task. In this paper, we evaluate ML tree-ensemble methods to predict QoS in the range of minutes with data collected from a cellular test network. We discuss radio environment characteristics and we showcase how these can be used to improve ML performance and further support the uptake of ML in commercial networks. Specifically, we use the correlations of the measurements coming from the radio environment by including information of prior vehicles to enhance the prediction of the target vehicles. Moreover, we are extending prior art by showing how longer prediction horizons can be supported.