  Unmanned aerial vehicle (UAV) millimeter wave (mmWave) technologies can
provide flexible link and high data rate for future communication networks. By
considering the new features of three-dimensional (3D) scattering space, 3D
velocity, 3D antenna array, and especially 3D rotations, a machine learning
(ML) integrated UAV-to-Vehicle (U2V) mmWave channel model is proposed.
Meanwhile, a ML-based network for channel parameter calculation and generation
is developed. The deterministic parameters are calculated based on the
simplified geometry information, while the random ones are generated by the
back propagation based neural network (BPNN) and generative adversarial network
(GAN), where the training data set is obtained from massive ray-tracing (RT)
simulations. Moreover, theoretical expressions of channel statistical
properties, i.e., power delay profile (PDP), autocorrelation function (ACF),
Doppler power spectrum density (DPSD), and cross-correlation function (CCF) are
derived and analyzed. Finally, the U2V mmWave channel is generated under a
typical urban scenario at 28 GHz. The generated PDP and DPSD show good
agreement with RT-based results, which validates the effectiveness of proposed
method. Moreover, the impact of 3D rotations, which has rarely been reported in
previous works, can be observed in the generated CCF and ACF, which are also
consistent with the theoretical and measurement results.
