  The millimeter wave bands are being increasingly considered for wireless
communication to unmanned aerial vehicles (UAVs). Critical to this undertaking
are statistical channel models that describe the distribution of constituent
parameters in scenarios of interest. This paper presents a general modeling
methodology based on data-training a generative neural network. The proposed
generative model has a two-stage structure that first predicts the state of the
link (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this
state into a conditional variational autoencoder that generates the path
losses, delays, and angles of arrival and departure for all the propagation
paths. Importantly, minimal prior assumptions are made, enabling the model to
capture complex relationships within the data. The methodology is demonstrated
for 28 GHz air-to-ground channels between UAVs and a cellular system in
representative urban environments, with training datasets produced through ray
tracing. The demonstration extends to both terrestrial base stations (mounted
at street-level) and aerial base stations (installed on rooftops).
