## 5 Performance Evaluation  
In this section, we present the performance evaluation of the proposed methodology. After describing the simulation scenario, we introduce the benchmark method as well as the performance metrics, followed by presenting the obtained results and corresponding discussions. The parameters used in the simulation campaigns are given in Table 1.  
The speed of the UAV BS is fixed in our model, as seen in Table 1. However, it is also an important parameter to considers, as it can have multiple impacts on the performance. For example, if the speed of the UAV BS is less than the average speed of the ground users, the UAV BS positions itself after the perfect time, which would degrade the overall performance as there is a delay in finding the optimal position. In the extreme scenario where the speed of the UAV BS is too less than the average speed of the users, a new positioning would be needed by the time the UAV BS arrives at the optimal position, as the users already moved, and the position of the UAV BS is not the optimal anymore. The same principle also applies to the case where the speed of the UAV BS is more than the average speed of the ground users, since there will be delay between the time that the UAV BS positions to an optimal point and the time that such point becomes the optimal position (because the users move slower than the UAV BS, which arrives the future optimal position early). The speed of the UAV BS requires a complex and detailed discussion, which would make this work lengthy and would also distract the main focus of the work. Therefore, we kept that discussion for a further study and focused only on how the UAV BS can be located in the case of moving users and NFZs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \multicolumn{2}{c}{**General**} \\ \hline Carrier frequency, \(f_{\mathrm{c}}\) & 1 GHz \\ Antenna directivity angle, \(\theta\) & 60\({}^{\circ}\) \\ Minimum UAV height, \(h_{\mathrm{min}}\) & 30 m \\ Maximum UAV height, \(h_{\mathrm{max}}\) & 120 m \\ Urban Area & 250 \(\times\) 250 m\({}^{2}\) \\ Total number of users, \(n_{\mathrm{t}}\) & 100 \\ Height from ground for all UEs & 1.5 m \\ Speed of light, \(c\) & \(3\times 10^{8}\) m/s \\ \(\eta\)LoS & 1.6 dB \\ \(\eta\)NLoS & 23 dB \\ Parameter of A2G path loss model, \(\Psi\) & 12.08 \\ Parameter of A2G path loss model, \(\zeta\) & 0.11 \\ Number of rotors, \(M\) & 4 \\ Fluid density of the air, \(\rho\) & 1.2 Kg/m\({}^{3}\) \\ Rotor disk radius, \(\beta\) & 0.25 m \\ Weight of the frame & 1.5 Kg \\ Weight of the battery and payload & 2 Kg \\ Bandwidth & 180 kHz \\ Transmit power, \(P_{\mathrm{t}}\) & 30 dBm (1 W) \\ On-board circuit power, \(P_{\mathrm{cu}}\) & 0.01 W \\ Duration of hovering of UAV, \(t_{\mathrm{h}}\) & 1 s \\ Duration to communication of UAV, \(t_{\mathrm{cm}}\) & 1 s \\ Velocity of the UAV, \(v\) & 30 m/s \\ Angular velocity, \(\omega\) & 40 rad/s \\ Drag coefficient & 0.025 \\ Rotor chord, \(c_{\mathrm{b}}\) & 0.022 m \\ Reference frontal area of the UAV & 0.192 m\({}^{2}\) \\ \hline \multicolumn{2}{c}{**\(Q\)-learning**} \\ \hline Discount rate, \(\gamma\) & 0.9 \\ Epsilon, \(\epsilon\) & 1 \\ Epsilon decay, \(\epsilon\)-decay & 0.95 \\ Learning rate, \(\alpha\) & 0.9 \\ Learning rate decay, \(\alpha\)-decay & \(1\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.