# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 4 Numerical Results and Analysis
### Simulation Discussion and Comparison
It reaches the performance level of a fully active RIS-UAV when the number of active elements in the hybrid UAV-RIS equals the total number of elements. Simulation results indicate the superior efficiency of the active UAV-RIS among all configurations in HOF, especially at higher altitudes. Figure 6 shows the throughput comparison between the proposed framework and other benchmarks. The simulation results reveal that the proposed UAV-RIS framework outperforms all others, even at higher altitudes, by dynamically optimizing signal paths and enhancing coverage through UAV positioning and RIS adjustment. The GBS (without UAV-RIS system) delivers satisfactory throughput at lower altitudes but becomes less effective at higher altitudes, making it unsuitable for high-altitude scenarios without UAVs or RIS support. For instance, at 300m height, the proposed UAV-RIS framework achieved 34.48% and 92.8% gains in throughput compared to UAV-only and RIS-only setups, respectively. In contrast, without the UAV-RIS framework, the throughput loss was approximately 66.67%. Overall, the suggested UAV-RIS framework provides the most robust solution to maintain improved throughput across various altitudes. Increasing the number of GUs in urban environments requires careful optimization of the UAV's altitude and RIS elements. Higher UAV altitudes improve LoS but increase path loss, requiring more RIS elements to maintain signal quality. Figure 7 shows the impact of different UAV altitudes and RIS elements on HO outage probability. Each RIS element actively reflects incident signals with varying phase shifts, and its performance depends on the relative positions of the UAV and the GUs. The simulation results demonstrate that dynamic optimization through the proposed UAV-RIS framework, utilizing the DDPG algorithm, effectively balances connectivity and HO performance in urban dense envi  
Figure 5: HOF vs. UAV-RIS configurations. ronments. For instance, at a UAV altitude of 350 m with 300 RIS elements, the proposed scheme reduces the OP by 20% compared to the scenario without DDPG. Figure 8 illustrates the OP as a function of distance for UAV-to-ground communication. In the without-UAV-RIS configuration, the OP is relatively high, especially as the distance increases. The RIS-only configuration achieves a moderate reduction in OP by reflecting signals to improve connectivity in LoS-blocked areas, but its effectiveness is limited over longer distances. The UAV-only configuration further reduces the OP by dynamically adjusting the UAV positions to maintain LoS links. However, challenges in HOF and mobility management persist due to UAV movement. The UAV-RIS configuration demonstrates superior performance in reducing OP by integrating UAV mobility with RIS optimization. Figure 6: Throughput vs. altitude. Figure 7: Impact of UAV altitude and RIS elements on OP. The proposed UAV-RIS system dynamically adjusts the UAV positioning and RIS phase shifts, leading to significant improvements in throughput, EE, and LoS probability. Figure 9 shows the LoS probability as a function of distance for UAV-to-ground communication in an urban environment, comparing four configurations: without UAV-RIS, RIS-only, UAV-only, and UAV-RIS. The without-UAV-RIS setup experiences a rapid decrease in LoS probability as distance increases. The RIS-only scenario improves performance but still shows a decreasing trend with distance. The UAV-only deployment, compared to the RIS-only and without-UAV-RIS scenarios, provides a better LoS probability due to the UAV's dynamic positioning. It is evident from the simulation outcomes that the proposed UAV-RIS setup achieves the highest LoS probability by combining UAV mobility and RIS, ensuring improved connectivity across all distances. This highlights the effectiveness of UAV-RIS in maintaining seamless connectivity and overcoming LoS blockages, leading to enhanced throughput in urban environments.