# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 4 Numerical Results and Analysis
### Simulation Discussion and Comparison  
Figure 3 compares the performance of the proposed UAV-RIS framework with three scenarios in terms of EE. The simulation results depict that the proposed framework is more energy-efficient than other benchmarks, as it reduces the energy required for signal transmission and enhances the overall performance of the FWNs. The UAV-only deployment consumes more energy than the RIS-only deployment at all altitudes due to the additional energy required for flight, mobility, and communication. The detailed analysis of the UAV-RIS framework reveals that its EE improves with increasing altitude. This is due to the higher elevation angle between the UAV-RIS and GUs, which reduces obstructions from buildings and other obstacles. Consequently, the LoS probability and the Rician factor increase, strengthening the channel gain and improving SINR. However, EE decreases when the UAV-RIS exceeds a certain altitude, as the increased distance to GUs leads to a higher path loss, ultimately degrading system performance. The impact of the number of GUs on EE is shown in Figure 4. The simulation outcomes clearly indicate that the system's EE increases with the number of GUs. The proposed DDPG-based UAV-RIS system outperforms the other three setups, primarily due to the joint optimization of UAV mobility and RIS phase shift adjustments, which enhance signal quality and reduce interference, particularly in dense urban environments. For instance, when there are 30 users, the UAV-RIS framework achieves gains of 40%, 31.4%, and 20% compared to the system without UAV-RIS, the RIS-only, and the UAV-only deployment, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum UAV-RIS speed & 72 km/h \\ \hline Maximum UAV-RIS height & 500 m \\ \hline Carrier frequency & 100 GHz \\ \hline Bandwidth & 10 GHz \\ \hline No. of randomly distributed GUs & 50 \\ \hline GUs speed & 3 km/h \\ \hline GBS to GU transmit power & 40 dBm \\ \hline UAV-RIS to GU transmit power & 30 dBm \\ \hline PL exponent for LoS and NLoS links & 2, 3 \\ \hline GBS antenna spacing & \(d=\frac{\lambda}{2}\) \\ \hline Rician factor (R) & 2 \\ \hline Discount factor & 0.9 \\ \hline Hidden layers & 2 \\ \hline Training networks’ learning rate & 0.001 \\ \hline Target networks’ learning rate & 0.001 \\ \hline Number of RIS elements & 100, 200, 300 \\ \hline RIS elements spacing (vertical and horizontal) & \(\frac{\lambda}{4}\) = 0.75 mm \\ \hline Number of episodes & 6000 \\ \hline Number of steps per episode & 2000 \\ \hline Experience replay buffer size & 150,000 \\ \hline Mini-batch size & 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameters. The hybrid UAV-RIS deployment is optimal for achieving superior system performance in terms of HOF and EE in urban environments. Figure 5 illustrates the effect of various altitudes on HOF for the three configurations: active UAV-RIS, passive UAV-RIS, and hybrid UAV-RIS. It is obvious from the simulation outcomes that at higher altitudes, the active UAV-RIS achieves better performance in reducing HOF because of its ability to amplify and direct the signals to the desired GUs, maintaining reliable connectivity. In contrast, passive UAV-RIS tends to increase the HOF probability at higher heights, as it has the property of reflection only without dynamic adjustment or amplification. This is because the UAV-RIS is able to achieve the desired GUs, maintaining reliable connectivity. Figure 4: EE vs. number of users. Figure 3: EE comparison of UAV-RIS scheme with benchmarks. results in a poor signal strength over greater distances between the UAV-RIS and GUs. Finally, the performance of the hybrid UAV-RIS, which falls between active and passive configurations is analyzed, resulting in a moderate HOF rate across different altitudes. Furthermore, Figure 5 demonstrates that the HOF performance of the hybrid UAV-RIS improves as the number of active elements increases.