## 4 Numerical Results and Analysis
The Tx trajectory was random, based on a Markov chain, and the Rx trajectory was based on the ST mobility model. Through the simulation, we acquired the analysis results and the simulation results, which were well-fitted. With the flight of the UAV, and the movement of the ground Rx, the ACF curve at \(t=3\) s, under the two trajectories, became steeper, as shown in Figure 6a, which shows that the channel became more complex, compared to the ACF curve at \(t=1\) s. Due to the movement of the UAV and the vehicle, the arrival and departure angles of the MPCs changed with time \(t\), which led to the non-stationarity of the channel. We found that the ACF curve of the linear trajectory was flatter, and that the channel was more stable than the random trajectory, which was due to the channel instability caused by the large angular variation of the random trajectory. As for the simulated parameters of Figure 6b, we kept the Tx and the Rx trajectories unchanged, and the accelerations at both ends were set to 1 m/s\({}^{2}\). Through simulation, it  
Figure 4: Time-variant PDP, based on different Tx moving trajectories for the MB case: (**a**) Tx for trajectory III, and Rx for trajectory I; (**b**) Tx for trajectory I, and Rx for trajectory I.  
Figure 5: Time-variant PDP, based on different Rx moving trajectories for the SB case: (**a**) Tx for trajectory III, and Rx for trajectory II; (**b**) Tx for trajectory III, and Rx for trajectory III. was found that when the \(v_{UAV}\) was large, the ACF curve would be steeper; therefore, we think that higher velocity of the UAV would lead to more drastic changes in the trajectory, which would lead to a decline in the stationarity of the channel. Figure 7 shows the 3D temporal ACFs of different trajectories. It shows the temporal ACF at the moment of flight of the drone in 10 seconds. To better show the relationship between the trajectory and temporal correlation, Figure 7a,b show the 3D trajectories projected to the XoY plane. The change in angle caused a decrease in the channel stationarity, and the characteristics are well reflected in Figure 7c,d. Figure 8 shows the spatial CCF of different distances; the CCF could reflect the spatial correlation of the antenna array. For the simulation parameters, the Rx trajectory was set as an unchanged static point, and the Tx trajectories were set as trajectory II and trajectory III, which simulated random slanting flight and linear level flight. Different trajectories of a UAV can lead to different spatial correlations. From the figure, two kinds of trajectories led to different spatial correlations in different time instants. Compared to the level flight, the height of the random slanting flight was larger; therefore, the slanting flight had the lower spatial correlation. Figure 8: Comparison between the spatial CCFs of the analytical model and those of the simulation model, with different trajectories and different time instants (\(V_{T}=35\) m/s, \(\beta_{E}^{R}=0\)). Figure 7: 3D temporal ACFs of different trajectories (moving time for 10 s, \(V_{T}=V_{R}=5\) m/s, \(a_{T}=a_{R}=0\) m/s\({}^{2}\)): (a) Case I: Tx for trajectory II, based on a Markov chain, and Rx for trajectory III; (b) Case II: Tx for random trajectory I, based on a Markov chain, and Rx for trajectory III; (c) 3D ACF based on Case I; (d) 3D ACF based on Case II. Figure 9: CDF of stationary interval, based on different velocities of the Tx and the Rx for trajectory III (\(V_{R}=2\) m/s, \(a_{T}=a_{R}=0\) m/s\({}^{2}\)). The channel measurements were conducted, taking into consideration the multi-mobility multi-trajectories of the Tx and the Rx. The satellite views and related trajectories are shown in Figure 10a. The measurement scene of the actual data was located on a bridge by Xingzhong Road. At the time of measurement, there were a large number of trees on both sides of the road, and a large number of vehicles moving. The Rx and the Tx were 50 m apart, both at a speed of 5 m/s, moving in opposite directions.