## 6 Simulation Results and Analysis  
For evaluation, we perform Monte-Carlo simulations. We present numerical results for outage probability, the transmission rate and BER performance of FSO links for various numbers of transmit/receive apertures and correlation values. We consider an FSO system with a receive aperture of size \(D_{0}=5\) cm and the wavelength of \(\lambda=1.55\) um. The link distance is assumed to be \(L=2\) km. The correlation length can, therefore, be approximated as \(d_{0}\approx\sqrt{\lambda L}=5.5\) cm. Set 3 relay stations, with each relay station's position randomly distributed within a circular area with a radius of 100 m. The transmission power of RF and FSO links is set to 10 dBm and 20 dBm, respectively. The beam PE is 0.2 mrad. To validate the performance of these models of millimeter-wave massive MIMO/FSO in a UAV system, the carrier frequency is 78 GHz. The grid size of the dictionary matrix is set to \(G_{R}=2N_{R}=64\), \(G_{T}=2N_{T}=128\). The maximum number of channel paths is set to \(Q_{max}=5\). The UAV speed is 0-80 km/h. We compared the performance of four methods in terms of FSO transmission rate, as shown in Figure 5. As can be seen, the dual-hop FSO in a UAV system with BiGRU-Attention neural model method offers the best performance in terms of the average FSO transmission rate. Compared with the DHPL-ML [8], LDPC-MIMO/FSO [7], and RA-FSO-RF [5; 6], the average transmission rates of FSO using BiGRU-Attention model were 1050 Mbps, 900 Mbps, 700 Mbps and 600 Mbps when SNR = 20 dB, respectively. The results indicate that the application of the BiGRU-Attention model in an FSO system can effectively solve the optical PE problem caused by UAV jitter. Although other methods can improve optical communication performance, the ability of FSO PE to handle complex jitter in FSO environments is limited. We compared the BER of four methods. As shown in Figure 6, the dual-hop FSO in a UAV system with the BiGRU-Attention neural model method offers the best performance in terms of the average BER. Compared with the DHPL-ML, LDPC-MIMO/FSO, and RA-FSO-RF, using the BiGRU-Attention model results in a decrease in the average BER, of \(10^{-5}\), \(10^{-4}\), \(10^{-3}\), and \(5\times 10^{-3}\), respectively, when SNR = 25 dB. Obviously, the BiGRU-Attention model achieves lower BER compared to other methods. In addition, all considered methods exhibit a trend of decreasing error rate with increasing SNR. This also reveals the significant impact of PE of UAVs in these systems. We investigated the performance of the system under consideration based on the probability of interruption. Figure 7 shows the relationship between outage probability and SNR ratio under different methods. Under the same SNR, the BiGRU-Attention model performed the best. For instance, compared with the DHPL-ML, LDPC-MIMO/FSO, and RA-FSO-RF, using the BiGRU-Attention model results in an outage probability of \(5\times 10^{-4}\), \(5\times 10^{-3}\), \(10^{-2}\), and \(10^{-1}\), respectively, when SNR = 20 dB. Figure 5: FSO transmission rate comparison of other methods with proposed method. According to the simulation results shown in Figure 8, the spectral efficiency of the proposed method in this paper reached 14 bps/Hz with SNR = 20 dB, which was the best performance. In comparison, the spectral efficiency of the RBOP [28] method was 12.5 bps/Hz, that of the REHS [27] method was 11 bps/Hz, that of the ZF-HBF [20; 21; 22; 23] method was 8 bps/Hz, and that of the Hy-BD [26] method was only 6 bps/Hz, the worst performance. We compared the BER of five methods, as shown in Figure 9. When SNR = 25 dB, the BER of the proposed method was \(10^{-5}\), which was the best compared with other methods, namely RBOP at \(0.5\times 10^{-4}\), REHS at \(0.7\times 10^{-4}\), ZF-HBF at \(1.2\times 10^{-4}\), and Hy-BD at \(5\times 10^{-4}\), the highest BER. Figure 6: BER comparison of other methods with the proposed method. Figure 7: Outage probability for different distributions of PE.