  We propose generative channel modeling to learn statistical channel models
from channel input-output measurements. Generative channel models can learn
more complicated distributions and represent the field data more faithfully.
They are tractable and easy to sample from, which can potentially speed up the
simulation rounds. To achieve this, we leverage advances in GAN, which helps us
learn an implicit distribution over stochastic MIMO channels from observed
measurements. In particular, our approach MIMO-GAN implicitly models the
wireless channel as a distribution of time-domain band-limited impulse
responses. We evaluate MIMO-GAN on 3GPP TDL MIMO channels and observe
high-consistency in capturing power, delay and spatial correlation statistics
of the underlying channel. In particular, we observe MIMO-GAN achieve errors of
under 3.57 ns average delay and -18.7 dB power.
