A commonly used assumption in the existing unmanned aerial vehicle (UAV) channel models is that the UAV flies along linear trajectories. However, this assumption does not always hold in realistic UAV communication scenarios. In this paper, we relax the linear trajectory restriction and present a temporally non-stationary channel model for UAV-to-ground communication scenario. The temporal non-stationarity of the channel stemming from time-varying heading directions of the UAV is properly modeled. This is achieved by combining a geometry-based stochastic model (GBSM) and an aeronautic random mobility model (RMM), which is introduced to characterize the movement pattern of UAVs. Based on UAV and ground station (GS) trajectories, time-varying model parameters and statistics including space-time-frequency correlation function (STF-CF), Doppler power spectrum density (PSD), delay PSD, and stationary interval are derived. The influences of the RMM-related parameters on the statistics are presented and analyzed. Some of those statistics are verified by measurements, showing the practicability of the model. The work presented in this paper is helpful in designing UAV communication system considering realistic UAV trajectories.

Sleep stage classification plays an important role in the diagnosis of sleep‐related diseases. However, traditional automatic sleep stage classification is quite challenging because of the complexity associated with the establishment of mathematical models and the extraction of handcrafted features. In addition, the rapid fluctuations between sleep stages often result in blurry feature extraction, which might lead to an inaccurate assessment of electroencephalography (EEG) sleep stages. Hence, we propose an automatic sleep stage classification method based on a convolutional neural network (CNN) combined with the fine‐grained segment in multiscale entropy. First, we define every 30 seconds of the multichannel EEG signal as a segment. Then, we construct an input time series based on the fine‐grained segments, which means that the posterior and current segments are reorganized as an input containing several segments and the size of the time series is decided based on the scale chosen depending on the fine‐grained segments. Next, each segment in this series is individually put into the designed CNN and feature maps are obtained after two blocks of convolution and max‐pooling as well as a full‐connected operation. Finally, the results from the full‐connected layer of each segment in the input time sequence are put into the softmax classifier together to get a single most likely sleep stage. On a public dataset called ISRUC‐Sleep, the average accuracy of our proposed method is 92.2%. Moreover, it yields an accuracy of 90%, 86%, 93%, 97%, and 90% for stage W, stage N1, stage N2, stage N3, and stage REM, respectively. Comparative analysis of performance suggests that the proposed method is better, as opposed to that of several state‐of‐the‐art ones. The sleep stage classification methods based on CNN and the fine‐grained segments really improve a key step in the study of sleep disorders and expedite sleep research.