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In this paper, a novel framework is proposed to enable air-to-ground channelmodeling over millimeter wave (mmWave) frequencies in an unmanned aerialvehicle (UAV) wireless network. First, an effective channel estimation approachis developed to collect mmWave channel information allowing each UAV to train alocal channel model via a generative adversarial network (GAN). Next, in orderto share the channel information between UAVs in a privacy-preserving manner, acooperative framework, based on a distributed GAN architecture, is developed toenable each UAV to learn the mmWave channel distribution from the entiredataset in a fully distributed approach. The necessary and sufficientconditions for the optimal network structure that maximizes the learning ratefor information sharing in the distributed network are derived. Simulationresults show that the learning rate of the proposed GAN approach will increaseby sharing more generated channel samples at each learning iteration, butdecrease given more UAVs in the network. The results also show that theproposed GAN method yields a higher learning accuracy, compared with astandalone GAN, and improves the average rate for UAV downlink communicationsby over 10%, compared with a baseline real-time channel estimation scheme.

Distributed Generative Adversarial Networks for mmWaveChannel Modeling in Wireless UAV Networks