## 5 Numerical Results  
Unless otherwise stated, \(\xi_{p}=1.2528\), \(\varphi_{AoA}=8\) mrad, \(d_{p}=1\) mm, \(d_{q}=3\) mm, and \(\varphi_{p}=0.175\) mrad. The following simulation results were obtained by using MATLAB 2022. When \(\xi_{q}=1.2528\), \(\xi_{f}=1.2528\), \(\alpha_{p}=2.49\), \(\beta_{p}=3.9\), \(\alpha_{q}=4.0\), \(\beta_{q}=1.9\), \(\alpha_{f}=4.0\), \(\beta_{f}=1.9\), Figure 2 illustrates the variation of ABER with SJR obtained by (21), and Monte Carlo simulation at \(\rho=1\). It can be observed from Figure 2 that the concordance between the analytical and simulation plots validates the accuracy of the derived expression. It can be observed from Figure 2 that as the receiving aperture N increases, the ABER shows significant improvement. However, with the increase of SJR, the degree of improvement decreases. This is because the jamming signal only jams one of the receiving apertures, and under high SJR, each receiving aperture can effectively receive signals. Since the additive white Gaussian noise is not considered in Figure 2, when SJR is high, increasing the number of receiving apertures cannot significantly improve the ABER when the SJR is large. When N = 5, the improvement of ABER with SJR increasing is not very obvious. The reason is that increasing the number of receiving apertures can effectively suppress the influence of jamming. At the same time, even though the additive Gaussian white noise is not considered, the FSO system are also affected by path loss, PE, and AT, which are a static variable, so the changing trend of ABER with SJR is not obvious. Let \(k=\frac{P_{l}}{N_{0}}=100\). In scenario 1, the relationship between SJR and ABER is illustrated in Figure 3, which is derived from (26) and (45). Similarly, the relationship between SJR and ABER for scenario 2 is depicted in Figure 4, derived from (28) and (51). From Figures 3 and 4, it can be observed that for SISO-FSO systems, as the jamming probability \(\rho\) increases, the ABER also increases; For SIMO-FSO systems, when SJR is greater than 20 dB, the ABER shows a positive correlation with \(\rho\). Conversely, when SJR is less than 20 dB, ABER decreases as \(\rho\) increases. This is because the use of SIMO technology enables effective signal recovery at high SJR. By comparing Figures 3 and 4, it is observed that jamming at the receiver has a significant impact on the system's BER at the RIS. For instance, when \(\rho=0.01\) and \(\overline{\tau}_{f}=60\) dB, ABER for Scenario 1 with SISO and SIMO are \(3.49\times 10^{-4}\) and \(4.77\times 10^{-5}\), respectively. For Scenario 2, ABER with SISO and SIMO are \(9.08\times 10^{-5}\) and \(1.35\times 10^{-6}\), respectively. This indicates that jamming at the receiver has a greater impact on the system compared to interference at the RIS. In Figure 5, we analyze the impact of different atmospheric turbulence on the ABER in two scenarios. The analytical plots closely align with the Monte Carlo simulations conducted, thereby confirming the accuracy of the derived expressions. It can be observed from Figure 5 that the system's overall performance improves as the turbulence ranges from strong (\(\alpha_{p}=4.0\), \(\beta_{p}=1.9\)) to moderate (\(\alpha_{p}=2.49\), \(\beta_{p}=3.9\)). Nevertheless, under the same turbulence parameters, the ABER performance for Scenario 1 is notably inferior to that of Scenario 2. This suggests that jamming at the RIS has a smaller impact on the system compared to jamming at the intended receiver of the legitimate signal. One possible reason for this is that the RIS distributes the power of the jamming signal. Figure 2: ABER versus SJR (dB) curves for an FSO system with a varying number of receiver apertures without considering additive Gaussian noise in Scenario 1. Figure 6 and 7 analyze the impact of the RIS location on the system ABER in two scenarios. Let \(\eta=L_{p}/L_{q}\), where \(L_{p}\) is the link length between S and RIS and \(L_{q}\) is the link length between RIS and the destination [19].