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In this paper, a novel framework is proposed to perform data-drivenair-to-ground (A2G) channel estimation for millimeter wave (mmWave)communications in an unmanned aerial vehicle (UAV) wireless network. First, aneffective channel estimation approach is developed to collect mmWave channelinformation, allowing each UAV to train a stand-alone channel model via aconditional generative adversarial network (CGAN) along each beamformingdirection. Next, in order to expand the application scenarios of the trainedchannel model into a broader spatial-temporal domain, a cooperative framework,based on a distributed CGAN architecture, is developed, allowing each UAV tocollaboratively learn the mmWave channel distribution in a fully-distributedmanner. To guarantee an efficient learning process, necessary and sufficientconditions for the optimal UAV network topology that maximizes the learningrate for cooperative channel modeling are derived, and the optimal CGANlearning solution per UAV is subsequently characterized, based on thedistributed network structure. Simulation results show that the proposeddistributed CGAN approach is robust to the local training error at each UAV.Meanwhile, a larger airborne network size requires more communication resourcesper UAV to guarantee an efficient learning rate. The results also show that,compared with a stand-alone CGAN without information sharing and two otherdistributed schemes, namely: A multi-discriminator CGAN and a federated CGANmethod, the proposed distributed CGAN approach yields a higher modelingaccuracy while learning the environment, and it achieves a larger average datarate in the online performance of UAV downlink mmWave communications.

Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks