## 3 Numerical Results
With the same \(d1\), the UAV with a height of 10 m receives the highest SER, and the UAV with a height of 20 m receives the lowest SER. The SERs of the signals received by UAVs at altitudes of 30 and 50 m are between 10 and 20 m, and the former is significantly higher than the latter. Although the performance of the UAV at 20 m is obviously better than that at other heights, considering the actual situation--a 20-m-high UAV cannot achieve a wider coverage range--we should make the best choice in the selection of the UAV height based on the combined benefits of the SER and coverage. In Figure 4, we reset \(d2\) from 50 to 100 m to increase the maximum altitude that the UAV can reach. At the same time, we narrowed the range of \(d1\) to 30-50 m and reset the values of \(H_{UAV}\) to 30, 50, and 100 m, respectively. One can see that, when \(d2\) is constant, the larger the height of UAV is, the lower the SER of the system will be. This conclusion applies to the communication system model in the low-altitude range higher than 22.5 m. After determining the accurate range of \(d1\) and conducting a comparison of specific values in the MATLAB simulation, it can be determined that when \(d1\) = 35 m, the system's SER reaches the lowest value. Figure 3: Symbol error rate versus \(d1\); \(K=50\). After the determination of the optimal distance of \(d1\), the following simulation analysis will be conducted for the value of \(d2\). All of the above simulations involved fixing \(d2\) at a specific value at which \(H_{UAV}\) could be flexibly changed (provided that \(d2\) was greater than or equal to \(H_{UAV}\)). The simulation results show that the SER of the system always decreases with the increase in \(H_{UAV}\) after the upper limit of \(d2\) is set. Therefore, we will keep the value of \(d2\) equal to that of \(H_{UAV}\)--that is, the UAV is active directly over the IRS--to simulate the relationship between \(d2\) and the SER. As shown in Figure 5, at different values of \(d1\), the SER of the signals received by the UAV gradually increases as \(d2\) increases from 30 to 200 m. This is because an increase in \(d2\) does not affect the size of the signal received from the base station for some factors, but as \(d2\) increases, the path loss on the NLoS link increases, as does the SER of the signal received by the UAV. Therefore, in order to keep the SER of the signal obtained from the UAV as low as possible, it is necessary to ensure that \(d2\) is reduced as much as possible while other values are equal. It can be seen from the above simulation that, with the same \(d2\), when the value of \(H_{UAV}\) is equal to that of \(d2\), the SER of the signal received by the UAV is the lowest, and the simulation test of IRS-UAV-USER is continued under this condition. Given that \(H_{UAV}\) is equal to \(d2\) (the UAV moves directly above the IRS), the value of \(H_{UAV}\) and the value of the included angle \(\alpha\) can be flexibly changed. By observing the SER of the signal received by the USER, the optimal deployment position of the UAV can be determined. Now, the height of the UAV and angle \(\alpha\) are taken as two independent variables affecting the USER's SER. The SER expression for the USER is calculated by using Formulas (7)-(9) and (18)-(21). With the SER as the dependent variable, the joint three-dimensional image of the influence of \(H_{UAV}\) and \(\alpha\) on the USER's SER is drawn. As shown in Figure 6, the surface is a three-dimensional image of \(H_{UAV}\), \(\alpha\), and the USER's SER, the plane is the maximum SER acceptable to the USER for cellular data, and the value is set to \(10^{-6}\). The intersection of the plane and the surface is the boundary of where the user can receive a good signal.