## 5 Simulation Results  
This section verifies the effectiveness of the proposed JDAPCOO algorithm through MATLAB simulations. The considered scenario has PDs randomly distributed in a \(1\) km \(\times\) 1 km geographical area, a UAV flies in a circle with a radius of 300 m at a fixed altitude with a fixed flight speed of 20 m/s, an LEO satellite and a ground 5G base station are used as cloud server data centers to collect the PDs' data, as shown in Figure 4 as the projection schematic of the simulation scenario in the horizontal plane. Due to space constraints, the detailed system model simulation parameters are shown in Table 1.  
Figure 3: Algorithm 3 flowchart. Furthermore, the relevant parameters of the algorithm proposed in this paper are shown in Tables 2 and 3, respectively. Figures 5 and 6 show the convergence of Algorithms 1 and 2 for different numbers of PDs with the 100 th time slot as an example, respectively. From the figures, we can see that as the number of PDs increases, the number of iterations required to find the global optimal solution will also surge, and the convergence time of the algorithm will become \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(I\) & 50\(\sim\)400 & \(W\) & 90 kHz \\ \(T\) & 100 s & \(\sigma^{2}\) & \(-\)127 dBm/Hz \\ \(\tau\) & 1 s & \(P_{u}\) & 46 dBm \\ \(H_{u}\) & 90 m & \(I_{u}\) & 75 \\ \(I_{0}\) & \(1.4\times 10^{-4}\) & \(I_{B}\) & 75 \\ \(H_{g}\) & 200 km & \(P_{\text{max}}\) & 40 dBm \\ \(l\) & 4 & \(R_{th}\) & 10 Mbps \\ \hline \hline \end{tabular}
\end{table} 
Table 1: System model simulation parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(T_{0}\) & 1 \\ \(V_{decrease}\) & 0.999 \\ \(v_{decrease}\) & 0.999 \\ \(T_{end}\) & 0.5 \\ \(L_{iteration}\) & 6932 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm 2 simulation parameters. Figure 4: Projection schematic of the simulation scenario in the horizontal plane. longer, so we can set a suitable limited number of iterations or annealing speed to obtain a suboptimal solution close to the optimal one. In order to illustrate the effectiveness of the JDAPCOO algorithm proposed in this paper, we compare the JDAPCOO algorithm proposed in this paper with the following three algorithms:  
* Equal power allocation algorithm [38]: The power of all PDs is set as the same value within the maximum power range. For the convenience of comparison with the JDAPCOO algorithm in this paper, the device association strategy still adopts the algorithm proposed in this paper. * Random power allocation algorithm [39]: The power of all PDs is determined randomly within the maximum power range. Like the equal power distribution algo  
Figure 5: Convergence of Algorithm 1. Figure 6: Convergence of Algorithm 2.  
rithm [38], the device association strategy still adopts the algorithm proposed in this paper. * Random device association algorithm: All PDs are randomly associated with the UAV or ground 5G base station. In order to show the superiority of the device association strategy in this paper, the power control scheme of PDs is the same as that in this paper. Figure 7 shows the curve of the total system energy efficiency changing with the number of PDs. The total energy efficiency of the four algorithms all shows an increasing trend with the increasing number of PDs. However, the JDAPCOO algorithm proposed in this paper is still improved compared with the other three algorithms. As the number of PDs continues to increase, the joint optimization algorithm proposed in this paper simultaneously optimizes the device association strategy and power control, thus bringing significant performance improvement.