## 6 Simulation Results  
In this section, we consider the downlink heterogeneous environment where we deployed the ABS to improve the coverage of a destroyed BS. We modeled the short-term fading as a flat fading and the large-scale fading using a probabilistic path loss model given in Equation (7). A UAV network was simulated with \(M\) ABSs, where each ABS supported a minimum of one aerial user. As ABSs were deployed close to each other, they introduced interference between users and their neighbors. To meet the QoS requirements of users, we increased the density of deployed ABSs from \(M=1\) to \(M=16\). We also assumed that all the ABSs were operating at a carrier frequency of 900 MHz. The allocated power in the range \([P_{\min},P_{\max}]\) was divided into 20 discrete steps with a step size of 1.5 dBm. The minimum sum-rate (\(C_{\min}\)) requirement for each user was set to 0.5 \(\mathrm{bps}/\mathrm{Hz}\). We considered a noise power equal to \(-174\) dBm/Hz. The remaining key simulation parameters are summarized in Table 3.  
The performance of the proposed scheme was tested to show the effectiveness of the proposed Q-learning-based prioritized user association scheme compared to the conventional signal-to-noise (SNR)-based user association scheme, where users are associated with the ABS receiving the maximum SNR. During simulations, we randomly deployed 40 users in the coverage area following a uniform distribution. We varied the number of deployed ABSs to obtain optimized ABS locations, user association, and transmit power. In this section, we verify the performance of the proposed scheme during an outage on the basis of the sum rate. The simulations were performed using MATLAB. First of all, we plotted the optimal deployment positions for four ABSs in Figure 4, where clusters were found using the \(k\)-means algorithm. Here, each ring shows the coverage range of the respective ABSs. For example, the red ring shows the coverage range of the red ABS and the users lying inside this ring are connected to that ABS, and similarly, for the green, blue, and black rings, respectively. Users were associated with an ABS on the basis of the maximum reward defined in Equation (12). The coverage range of each ABS was 225 meters, that is, the radius of each ABS. The ABSs were deployed to minimize the coverage overlap among ABSs. The altitude of ABSs was variable as shown with the dotted lines, where each ABS moved between the defined altitude range. Figure 4: Optimized 3D ABS deployment of ABSs and users using the proposed scheme. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Users (\(U\)) & 40 \\ ABS (\(M\)) & 16 \\ \(P_{\min}\) & \(-20\) dBm \\ \(P_{\max}\) & 25 dBm \\ Step size & 1.5 \\ \(h_{\min}\) & 100 \\ \(h_{\max}\) & 600 \\ \(radius(\mathrm{R})\) & 250 m \\ \(\alpha\) & 0.5 \\ \(\beta\) & 0.9 \\ maxIteration & 50,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. Figure 5 shows the sum-rate performance for the proposed prioritized user association scheme compared to a conventional SNR-based association scheme. In the proposed scheme, the Q-learning algorithm was implemented to optimize the power compared to the conventional scheme, which, in turn, considered the real-time environment resulting in significant performance gain. Moreover, we can notice that the sum rate increases as the number of ABSs increases but if it increases above four then the sum rate begins to decrease. The reason is that as the number of ABSs increases, interference among them also increases, which lowers the sum rate. By comparing these two schemes, we achieved a maximum value for the average sum rate of around 36.55 bps/Hz compared to the conventional scheme. Finally, we compared the user outage performance for both schemes in Figure 6. The simulation results show that the number of associated users is maximum when the number of deployed ABSs are four, under the proposed scheme, as the percentage of outage is far less in that case.