  Wireless vehicular communication will increase the safety of road users. The
reliability of vehicular communication links is of high importance as links
with low reliability may diminish the advantage of having situational traffic
information. The goal of our investigation is to obtain a reliable coverage
area for non-stationary vehicular scenarios. Therefore we propose a deep neural
network (DNN) for predicting the expected frame error rate (FER). The DNN is
trained in a supervised fashion, where a time-limited sequence of channel
frequency responses has been labeled with its corresponding FER values assuming
an underlying wireless communication system, i.e. IEEE 802.11p. For generating
the training dataset we use a geometry-based stochastic channel model (GSCM).
We obtain the ground truth FER by emulating the time-varying frequency
responses using a hardware-in-the-loop setup. Our GSCM provides the propagation
path parameters which we use to fix the statistics of the fading process at one
point in space for an arbitrary amount of time, enabling accurate FER
estimation. Using this dataset we achieve an accuracy of 85% of the DNN. We use
the trained model to predict the FER for measured time-varying channel transfer
functions obtained during a measurement campaign. We compare the predicted
output of the DNN to the measured FER on the road and obtain a prediction
accuracy of 78%.
