## 3 Simulation Results and Discussion  
In order to calculate the effect of angular sampling on the TRP based on the proposed algorithm, simulations were performed. The corresponding analyses and discussions are also described in this section. In the simulations, the test distance satisfied the requirement for the far-field distance. Table 1 shows the impact of the sampling step on the normalized EIRP patterns of different HPBWs. It can be seen that as the step increased, the normalized EIRP patterns exhibited different degrees of distortion. For HPBW \(=25^{\circ}\), the normalized EIRP pattern could be basically restored when the sampling step was \(=10^{\circ}\). However, when the step increased to \(15^{\circ}\), both the main beam and the sidelobe were distorted. For HPBW \(6^{\circ}\), the degree of reduction in the normalized EIRP patterns was acceptable when the sampling step was \(5^{\circ}\). When it increased to \(10^{\circ}\), the distortion was already obvious; as shown in the figure, almost all sidelobes disappeared. This distortion was more obvious when the sampling step was \(15^{\circ}\). Although the sampling step was smaller and the restored EIRP pattern was better, it can be seen from the number of sampling points \(P\) in the table that the smaller the step was, the greater the number of sampling points would be, which meant that the cost of measurement would be higher. On the contrary, the larger the sampling step, the lower the number of sampling points, the lower the measurement accuracy, and the lower the cost. Therefore, determining an appropriate sampling step to ensure that the normalized EIRP patterns of different HPBWs can be well restored is very important. This also means finding a balance between measurement cost and accuracy to improve measurement efficiency. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sampling Config** & **HPBW = 25\({}^{\circ}\)** & **HPBW = 6\({}^{\circ}\)** \\ \hline Normalized EIRP patterns & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sampling setup with the normalized EIRP patterns. To further clarify the impact of the sampling step on the TRP of different HPBWs, we simulated the variations in \(\Delta\)TRP with different HPBWs and steps for different methods, as shown in Figure 4a-c. From the figure depicting the results of the spherical grid method, it can be seen that when the HPBW was greater than \(30^{\circ}\), \(\Delta\)TRP was small and remained almost unchanged as the sampling step increased. However, when the HPBW was less than \(30^{\circ}\), \(\Delta\)TRP changed significantly with the sampling step. When the step was less than 12, \(\Delta\)TRP was almost zero, but as the step continued to increase, \(\Delta\)TRP also gradually increased. In general, the narrower the HPBW of spurious emissions, the greater the effect of the sampling step on \(\Delta\)TRP. Figure 4b,c depict \(\Delta\)TRP for the two-cut and three-cut methods. The values of \(\Delta\)TRP for these methods were generally higher than those for the spherical grid method, but the trend of the change in \(\Delta\)TRP with the HPBW and sampling step was the same. Compared with those in the three-cut method, the changes in the two-cut method were more significant. As shown in Figure 4b,c, \(\Delta\)TRP increased with the decrease in the HPBW and the increase in the sampling step. This was mainly because the narrower the HPBW of spurious emissions is, the higher the beam resolution will be. Therefore, when the sampling step remained constant, \(\Delta\)TRP increased with the decrease in the HPBW. Meanwhile, the larger the sampling step was, the more sparse the sampling points there would be, which led to an increase in \(\Delta\)TRP. When the HPBW increases to a certain extent, the impact of the sampling step on \(\Delta\)TRP can almost be ignored. From the above analysis, it can be seen that the TRP mainly depended on the HPBW of the spurious beam and the sampling step.