## 4 Simulations and Analysis
For example, when \(\theta_{FoV}=40\) mrad, the average BER for \(\sigma_{a}=10\), \(13\), \(16\), \(19\) mrad are \(3.5\times 10^{-4}\), \(8.5\times 10^{-4}\), \(2.1\times 10^{-3}\) and \(4.6\times 10^{-3}\), respectively. At the same time, it is found that the slight upward trend is related to orientation deviations, i.e., the lower the orientation deviation, the more pronounced the increase is. The orientation deviation has been the dominant impactor for the performance of the average BER at this moment. Figure 4 depicts the average BER for varying \(r_{L}/r_{r}\) under different detection techniques. This figure shows that considered system with the heterodyne detection achieves lower average BER compared to the IM/DD, which is more favorable for drone-assisted FSO communication. Specifically, the average BER under the heterodyne detection and the IM/DD techniques for \(r_{L}/r_{r}=30\) are \(2.7\times 10^{-7}\) and \(2.2\times 10^{-4}\) when \(P_{t}=30\) dBm,  
Figure 3: Average BER versus \(\theta_{FoV}\) under various \(\sigma_{a}\). Figure 2: Average BER versus \(P_{t}\) under AoA fluctuations and pointing errors. respectively. Besides, when changing the ratio of beam radius to receiver aperture radius, the change in the performance of the considered system with the heterodyne detection technique is more evident than that with the IM/DD technique. This phenomenon is because the heterodyne detection has higher conversion gain and better filtering of background light compared to the IM/DD. Moreover, the energy of the received optical beam increases and the average BER decreases as the ratio of beam radius to receiver aperture radius increases. For the heterodyne detection technique, the average BERs are \(4\times 10^{-2}\), \(3.6\times 10^{-5}\), and \(2.7\times 10^{-7}\), respectively, when \(r_{L}/r_{r}\) are 10, 20, and 30 at \(P_{l}=30\) dBm. Finally, the Monte Carlo results fit the analytical results well. The impact of link distance on the average BER as a function of atmospheric conditions is presented in Figure 5. It is apparent that the average BER increases with increasing link distances, e.g., for weak turbulence and \(P_{l}=2\) dBm, the average BERs for \(L=1,2,4\) km are \(1.5\times 10^{-4}\), \(2.1\times 10^{-4}\) and \(3.7\times 10^{-4}\), respectively. Furthermore, when the degree of turbulence influence increases, the average BER also increases, i.e., at \(P_{l}=10\) dBm and \(L=2\) km, the average BERs from weak to strong turbulence are \(1.2\times 10^{-4}\), \(1.3\times 10^{-4}\), and \(1.4\times 10^{-4}\), respectively. Previously, the influence of different factors is already discussed on the average BER. Here, the influence of these factors on the ergodic capacity is further investigated. The influence of AoA fluctuations and the receiver FoV on the ergodic capacity are demonstrated in Figure 6. First, the ergodic capacity increases as \(P_{l}\) increases, e.g., the ergodic capacity  
Figure 4: Average BER versus \(P_{l}\) under different detection techniques and various \(r_{L}/r_{r}\). Figure 5: Average BER versus \(P_{l}\) under various \(L\) and turbulence conditions. for \(P_{t}=10\) dBm is 37.5 bit/s/Hz. The ergodic capacity for \(P_{t}=30\) dBm is 44.1 bit/s/Hz, for the considered system without AoA fluctuations. Moreover, it is noticeable that the ergodic capacity is higher for the system without AoA fluctuations. Furthermore, the ergodic capacity increases and then decreases as \(\theta_{FoV}\) increases when AoA fluctuations are considered. For example, the ergodic capacity for \(\theta_{FoV}=30\), 80, and 40 mrad are 33.1, 35.9, and 36.4 bit/s/Hz when \(P_{t}=10\) dBm, respectively. Finally, the Monte Carlo simulation results certify the theoretical results. The ergodic capacity for versus \(\theta_{FoV}\), under various orientation deviations, is presented in Figure 7. The analytical results match the Monte Carlo results, thus verifying that the closed-form formula for the ergodic capacity is highly accurate.