## 3 Numerical Results  
Through the calculation of the above formula, it can be concluded that the SER at the receiving end of the UAV model of the BS-IRS-UAV system is affected by many parameters, including the number of reflectors \(K\), the distance _d1_ between the BS and the IRS, the distance _d2_ between the IRS and the UAV, and the distance \(d\) between the BS and the UAV. However, when the UAV reaches a certain height, the angle between the UAV and the BS is too large, which makes the signal that is directly transmitted from the base station to the UAV in the LoS link very weak. When digitally superimposed with the path attenuation power, the received power at the receiving end of the UAV is basically 0(whether it is completely 0 depends on the flight altitude of the UAV). The received power of the LoS link is much smaller than that of the NLoS link. Therefore, in this section, all numerical results will be analyzed in order to demonstrate the system performance in terms of parameters other than the distance between the BS and UAV. Table 1 gives the values of some constant parameters that will be used in the simulation test in the form of constant values in the simulation analysis. Next, we analyze the influence of other variables on the receiving power of the UAV end. Figure 2 shows the influence of the number of reflective elements of the IRS on the SER of the signal received at the UAV end. The height of the UAV was set to 10, 20, and 50 m, respectively, and the relationships between the SER of the UAV's received signal and the number of reflectors \(K\) were compared at different heights. It can be seen in Figure 2 that the height of the UAV does not affect the linear relationship between the received signal's SER at the receiving end and the number of elements \(K\). Selecting a height near the critical value as a reference, it is found that the SER of the received signal of the UAV at the height of 20 m is significantly better than those at the height of 10 and 50 m. When the number of reflective elements \(K\) of the IRS gradually increases, the SER of the UAV's received signals at different altitudes shows a downward trend. It can be seen that, when the number of reflective elements \(K\) is set to 50, the SER at the receiving end has reached an ideal level. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Carrier frequency & \(f\) & 2 GHz \\ Base station transmitting power & \(P_{T}\) & 46 dBm \\ Dip angle of the base station & \(\theta_{eilt}\) & \(15^{\circ}\) \\ Angle between the base station and IRS & \(\theta\) & \(0^{\circ}\) \\ The angle of 3 dB & \(\theta_{3 dB}\) & \(10^{\circ}\) \\ Signal phase of the LoS link & \(\varphi_{0}\) & 0 \\ Signal phase of the NLoS link & \(\varphi_{k}\) & 0 \\ Reflection power loss of IRS & \(PL_{IRS}\) & 1 dB \\ The critical parameters & SLA & 20 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter symbols and values. Figure 2: Symbol error rate versus the number of IRS elements. We fixed the number of IRS elements at 50, and then tested the linear relationship between the distance \(d1\) (for the convenience of simulation, the values of the distances between all components of the IRS and the base station were equal by default, and are uniformly represented by \(d1\) and \(d2\)) between the base station and the IRS and the SER. At the same time, we continued to add the UAV height as the third variable in the system simulation test. Figure 3 shows the linear relationship between \(d1\) and the system SER at different UAV heights, and it shows the UAV heights under four conditions: 10, 20, 30, and 50 m. Four different heights were selected between the heights close to the critical point and those far from the critical point to verify the optimal value of \(H_{UAV}\). As we can see in the figure, whether \(H_{UAV}\) is larger than 22.5 m or not does not affect the linear relationship between \(d1\) and the SER (first decreasing and then increasing).