  In this paper, a novel framework is proposed to enable air-to-ground channel
modeling over millimeter wave (mmWave) frequencies in an unmanned aerial
vehicle (UAV) wireless network. First, an effective channel estimation approach
is developed to collect mmWave channel information allowing each UAV to train a
local channel model via a generative adversarial network (GAN). Next, in order
to share the channel information between UAVs in a privacy-preserving manner, a
cooperative framework, based on a distributed GAN architecture, is developed to
enable each UAV to learn the mmWave channel distribution from the entire
dataset in a fully distributed approach. The necessary and sufficient
conditions for the optimal network structure that maximizes the learning rate
for information sharing in the distributed network are derived. Simulation
results show that the learning rate of the proposed GAN approach will increase
by sharing more generated channel samples at each learning iteration, but
decrease given more UAVs in the network. The results also show that the
proposed GAN method yields a higher learning accuracy, compared with a
standalone GAN, and improves the average rate for UAV downlink communications
by over 10%, compared with a baseline real-time channel estimation scheme.
