  The provision of reliable connectivity is envisioned as a key enabler for
future autonomous driving. Anticipatory communication techniques have been
proposed for proactively considering the properties of the highly dynamic radio
channel within the communication systems themselves. Since real world
experiments are highly time-consuming and lack a controllable environment,
performance evaluations and parameter studies for novel anticipatory vehicular
communication systems are typically carried out based on network simulations.
However, due to the required simplifications and the wide range of unknown
parameters (e.g., Mobile Network Operator (MNO)-specific configurations of the
network infrastructure), the achieved results often differ significantly from
the behavior in real world evaluations. In this paper, we present Data-driven
Network Simulation (DDNS) as a novel data-driven approach for analyzing and
optimizing anticipatory vehicular communication systems. Different machine
learning models are combined for achieving a close to reality representation of
the analyzed system's behavior. In a proof of concept evaluation focusing on
opportunistic vehicular data transfer, the proposed method is validated against
field measurements and system-level network simulation. In contrast to the
latter, DDNS does not only provide massively faster result generation, it also
achieves a significantly better representation of the real world behavior due
to implicit consideration of cross-layer dependencies by the machine learning
approach.
