# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 4 Simulation Results and Discussion
### Convergence Performance  
To delve into the convergence performance of algorithms, we conducted simulations in both small-area (500 m \(\times\) 500 m) and large-area (4000 m \(\times\) 4000 m) scenarios. In the 500 m \(\times\) 500 m area scenario, we envision a short-lived communications outage in this small area, which would be suitable for the deployment of subminiature UAVs. Eight UAVs were deployed to execute mission tasks, with a communication radius set at 100 m. To assess the effectiveness of the UAV deployment, we set the error threshold between the UAV's horizontal coordinates and the Euclidean distance of the target deployment point to 10m. In the 4000 m \(\times\) 4000 m area scenario, 10 UAVs were deployed, with a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(N\) & Number of UAVs & 8–12 \\ \(M\) & Number of the ground users awaiting rescue & 200 \\ \(d_{\min}\) & UAVs safe distance & 100 m \\ \(d_{\max}\) & Maximum communication distance of UAVs & 100–750 m \\ \(f_{0}\) & Carrier frequency & 1440 MHz \\ \(T_{1}\) & Path loss threshold & 150 dB \\ \(h_{\mathit{msta}}\) & The effective height of the user equipment antenna on the rescue surface & 1.5 m \\ \(h_{\mathit{sta}}\) & The effective height of the UAVs station antenna & 30–200 m \\ \(\omega\) & The number of chromosomes & 30 \\ \(\triangleleftharpoons\) & The crossover probability & 0.9 \\ \(\rhd\) & The mutation probability & 0.09 \\ \(\gamma_{1}\),\(\gamma_{2}\) & Number of iterations & 1500, 4000 \\ \(P_{\mathrm{t}}\) & UAVs launch power & 20–60 dBm \\ \(P_{\mathrm{r}}\) & Power received by ground users to be rescued & \(-80\) dBm \\ \(F_{\mathrm{BW}}\) & Bandwidth frequency & 40 MHz \\ \(\sigma\),\(\tau\) & Random variable & 0–1 \\ \(E_{\mathit{LIAV}}\) & The power of a single UAV base station & 600 Wh \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameter settings.  
communication radius of 750 m. To assess the effectiveness of the UAV deployment, we set the error threshold between the UAV's horizontal coordinates and the Euclidean distance of the target deployment point to 50 m.  
Figure 4a-c shows the fitness curve of UAV deployment in the small area scenario, where Figure 4d depicts the fitness curve in the large area scenario. It is observed that our proposed algorithm converges after a certain number of iterations, which demonstrates the effectiveness of our method in achieving rapid deployment for emergency communication UAVs. As illustrated in Figure 4a, both the SAIBFGS algorithm and the LAESGA algorithm achieve higher accuracy in solving the 500 m \(\times\) 500 m area deployment, enabling more accurate deployment overall. In Figure 4c, the SAIBFGS is difficult to converge in a 2500 m \(\times\) 2500 m area. In contrast, Figure 4d shows that the SAIBFGS algorithm struggles to handle large area deployment tasks, while the LAESGA algorithm effectively solves such tasks. Although SGA converges quickly, its convergence time is too early to reach the specified accuracy. So, while the area is less than 1500 m \(\times\) 1500 m, we recommend using the SAIBFGS algorithm to calculate the optimal horizontal deployment coordinates of UAV base stations.