  Unmanned aerial vehicle (UAV) is steadily growing as a promising technology
for next-generation communication systems due to their appealing features such
as wide coverage with high altitude, on-demand low-cost deployment, and fast
responses. UAV communications are fundamentally different from the conventional
terrestrial and satellite communications owing to the high mobility and the
unique channel characteristics of air-ground links. However, obtaining
effective channel state information (CSI) is challenging because of the dynamic
propagation environment and variable transmission delay. In this paper, a deep
learning (DL)-based CSI prediction framework is proposed to address channel
aging problem by extracting the most discriminative features from the UAV
wireless signals. Specifically, we develop a procedure of multiple Gaussian
Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and
pre-training utilization incorporated with an autoencoder-based deep neural
networks (DNNs). To evaluate the proposed approach, real data measurements from
an UAV communicating with base-stations within a commercial cellular network
are obtained and used for training and validation. Numerical results
demonstrate that the proposed method is accurate in channel acquisition for
various UAV flying scenarios and outperforms the conventional DNNs.

Signal Processing