## 6 Simulation Results  
In this section, the numerical results from the Monte Carlo simulations with \(10^{7}\) samples are provided to show the validity of the analytical results and provide some insights into how different parameters affect the proposed system performance. More specifically, we show the impacts of various impulsive noise, turbulence conditions, and pointing errors on the performance of the proposed system under two detection techniques, heterodyne and direct detection, which are denoted by \(r=1\) and \(r=2\), respectively. For the FSO link, we set \((\alpha,\beta)\) as \((4.6,7.1)\), \((2.4,4.5)\), and \((1.4,3.5)\), corresponding to weak, moderate, and strong turbulence, respectively. Similarly, for the PLC link, the impulsive noise frequency and intensity vary from weak to strong levels in terms of \(\rho\) and the ratio \(\sigma_{1}^{2}/\sigma_{\text{G}}^{2}\). Also, we have \(\xi=1.5\) while \(\mu_{1}=0\), \(\sigma_{1}^{2}=0.18\), and \(\varphi=\varphi^{\prime}=1.55\). The values of \(n^{\prime},\delta\), and \(q_{k}\) are (1,1,1) and (1,1,1/2) when BPSK and QPSK modulations are used, respectively. Additionally, in all figures, the theoretical results perfectly match the Monte-Carlo simulation results, demonstrating the accuracy of the analytical derivation. In Figure 2, we show the outage probability versus \(\gamma_{\text{FSO}}\) for both cases of \(r=1\) and \(r=2\) under weak, moderate, and strong turbulence conditions. For a fixed average SNR of the PLC link, we can see that the outage probability significantly depends on the parameters of the FSO link. It can be observed that with the direct detection technique, the outage probability of the proposed system is higher than when the heterodyne detection is employed. Additionally, stronger turbulence leads to a higher outage probability. In Figure 3, we show the impacts of various PLC link parameters on the outage probability of the proposed system when the average SNR of the FSO link is fixed at 50 dB. More specifically, three scenarios are considered, where the strong, moderate, and weak impulsive noise conditions are given as \(\rho=0.1,0.4\), and \(0.8\), respectively. The outage probability of the system increases with the stronger impulsive noise level, while weak impulsive noise achieves lower outage probability. On the other hand, the higher the impulsive noise is compared with the background noise, the higher the outage probability is, as \(\sigma_{1}^{2}/\sigma_{\text{G}}^{2}\) changes from a low value of 0.2 to 0.5. Furthermore, the BER of the proposed system is given in Figure 4 with the average SNR of the FSO link versus different values for the turbulence conditions and two modulation schemes such as quadrature phase-shift keying (QPSK) and binary phase-shift keying (BPSK). It is observed that the average BER performance provides a perfect match to the closed-form expression of our analytical derivation. As expected, the BPSK modulation achieves better performance in comparison with the QPSK modulation. Meanwhile, as the turbulence of the FSO link raises, the BER performance becomes worse. In Figure 5, we present the BER of the proposed system versus the average SNR of the PLC link under various channel conditions, in which the impulsive noise change from weak to strong, described by the values of \(\rho\) and \(\sigma_{1}^{2}/\sigma_{G}^{2}\), respectively. Moreover, the average SNR value of the FSO link is fixed at 40 dB while the modulation type is BPSK. From Figure 5, it is clear that both FSO and PLC links have significant impact on the overall performance and an increasing in the quality of individual link can result in an improvement of the BER result. Moreover, high values of impulse noise probability and power of noise lead to higher error probability. Also, the with the fixed SNR value of the FSO link, when the SNR of PLC link approach relatively high values, the FSO link become dominant factor and the BER performance can only achieve a value of \(10^{-4}\).