  Multi-Mobile Network Operator (MNO) networking is a promising method to
exploit the joint force of multiple available cellular data connections within
vehicular networks. By applying anticipatory communication principles, data
transmissions can dynamically utilize the mobile network with the highest
estimated network performance in order to achieve improvements in data rate,
resource efficiency, and reliability. In this paper, we present the results of
a comprehensive real-world measurement campaign in public cellular networks in
different scenarios and analyze the performance of online data rate prediction
based on multiple machine learning models and data aggregation strategies. It
is shown that multi-MNO approaches are able to achieve significant benefits for
all considered network quality and end-to-end indicators even in the presence
of a single dominant MNO. However, the analysis points out that anticipatory
multi-MNO communication requires the consideration of MNO-specific machine
learning models since the impact of the different features is highly depending
on the configuration of the network infrastructure.
