  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 link state
(line-of-sight, non-line-of-sight, or outage), and subsequently feeds this
state into a conditional variational autoencoder (VAE) that generates the path
losses, delays, and angles of arrival and departure for all the propagation
paths. 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 standard base stations (installed at street level and downtilted) as well
as dedicated base stations (mounted on rooftops and uptilted). The proposed
approach is able to capture complex statistical relations in the data and it
significantly outperforms standard 3GPP models, even after refitting the
parameters of those models to the data.
