  Statistical channel models are instrumental to design and evaluate wireless
communication systems. In the millimeter wave bands, such models become acutely
challenging; they must capture the delay, directions, and path gains, for each
link and with high resolution. This paper presents a general modeling
methodology based on training generative neural networks from data. The
proposed generative model consists of a two-stage structure that first predicts
the state of each 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
its propagation paths. Importantly, minimal prior assumptions are made,
enabling the model to capture complex relationships within the data. The
methodology is demonstrated for 28GHz air-to-ground channels in an urban
environment, with training datasets produced by means of ray tracing.
