  In this paper, a novel framework is proposed to perform data-driven
air-to-ground (A2G) channel estimation for millimeter wave (mmWave)
communications 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 stand-alone channel model via a
conditional generative adversarial network (CGAN) along each beamforming
direction. Next, in order to expand the application scenarios of the trained
channel model into a broader spatial-temporal domain, a cooperative framework,
based on a distributed CGAN architecture, is developed, allowing each UAV to
collaboratively learn the mmWave channel distribution in a fully-distributed
manner. To guarantee an efficient learning process, necessary and sufficient
conditions for the optimal UAV network topology that maximizes the learning
rate for cooperative channel modeling are derived, and the optimal CGAN
learning solution per UAV is subsequently characterized, based on the
distributed network structure. Simulation results show that the proposed
distributed CGAN approach is robust to the local training error at each UAV.
Meanwhile, a larger airborne network size requires more communication resources
per UAV to guarantee an efficient learning rate. The results also show that,
compared with a stand-alone CGAN without information sharing and two other
distributed schemes, namely: A multi-discriminator CGAN and a federated CGAN
method, the proposed distributed CGAN approach yields a higher modeling
accuracy while learning the environment, and it achieves a larger average data
rate in the online performance of UAV downlink mmWave communications.
