## 5 Simulation Results
### Simulation of the SULA Model  
The Doppler effect is caused by relative motion. Figure 9a is the influence of Doppler effect on the received signal envelope when the speed is 80 km/h in the Rayleigh channel. When the speed is 120 km/h, the influence of Doppler effect is shown in Figure 9b.  
Figure 8: The relationship between uncertainty and shadowing standard deviation.  
Figure 9: Influence of Doppler on signal fading at different velocity. (**a**) Influence of Doppler effect on channel fading at 80 km/h flight speed; (**b**) Influence of Doppler effect on channel fading at 120 km/h flight speed.  
According to the simulation results, the maximum envelope attenuation of the UAV at high speed is greater than that at low speed. It can be seen that the faster the UAV goes, the stronger the signal attenuation is and vice versa. As the speed increases, the influence of the Doppler effect on the envelope of the received signal becomes more and more obvious. In the process of information transmission, a large number of consecutive bits are destroyed due to fading and Doppler frequency shift, which leads to an increase in the bit error rate.  
Figure 10 shows the BER performance of a single antenna receiver, a 16-element diversity receiver, and a 16-element beamforming receiver under three different path numbers.  
It can be seen from Figure 10 that the bit error rate of a single antenna channel is significantly higher than the other two methods under the same channel conditions. When the signal-to-noise ratio is small, the channel error rate of the multi-antenna diversity method is similar to the SULA channel established in this paper. However, because the multi-antenna diversity technology does not separate the directional path of each unit, the influence of Doppler frequency shift on the path still exists, which will lead to frequency domain interference and increase in bit error rate. Therefore, with the increase of the signal-to-noise ratio, the bit error rate of the SULA channel decreases significantly, but the bit error rate of the multi-antenna diversity channel changes slightly. In contrast, the SULA model uses uniform circular array beamforming technology to separate the Doppler frequencies of different arrival paths and then correct them separately, which can better eliminate the influence of Doppler frequency shift on the channel. In particular, in Figure 10, the orange line represents the system bit error rate curve when the number of paths is larger. The bit error rate of channels using single-antenna and multi-antenna diversity technologies increases with the increase in the number of paths. However, the channel model proposed in this paper first separates the paths, and then compensates for the Doppler shift. As the number of paths increases, the algorithm gain effect becomes more significant, and the system error rate is still decreasing.  
The BER performance of different normalized Doppler frequencies in the three cases is shown in Figure 11. The number of \(u\) is fixed at 3, the antenna unit is 16, and the SNR is 15 dB. It can be seen that the performance of multi-antenna diversity method and beamforming method is much better than that of single antenna method. However, with the increase of the normalized Doppler frequency, the bit error rate of the multi-antenna diversity method increases sharply. Correspondingly, the performance of the SULA model remains stable under different Doppler extensions. Therefore, when the UAV is carrying out data transmission, even if the communication environment is constantly changing and becoming more complex, the bit error rate of this model is still kept in a small range. This ensures the reliability of communication when emergency work is in progress.  
Figure 10: The relationship between the BER and the SNR of the three methods.