# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 5 Conclusions and Future Work  
The integration of RIS and UAVs is anticipated to enhance wireless network coverage, create additional propagation paths around obstacles, and establish LoS links with distant  
Figure 10: Convergence behavior of the proposed DDPG-based UAV-RIS optimization framework under different learning rates \(\kappa\) and soft update coefficients \(\tau\).  
Figure 9: LoS probability vs. distance for UAV-to-ground communication.  
GUs, making it a promising technology for FWNs. This study investigated the performance of various frameworks and demonstrated that the proposed UAV-RIS framework, utilizing the DDPG scheme, outperformed the benchmarks in improving connectivity and throughput for GUs. The integration of UAV-RIS with the DDPG-based scheme leads to a significant improvement in addressing the challenges posed by high-mobility scenarios in FWNs. The proposed framework effectively improves LoS probability to GUs by reducing path loss, which is crucial for maintaining reliable communication in urban dense environments. Furthermore, the proposed DDPG algorithm, through the training of two main networks (actor network and critic network), adaptively optimizes the UAV trajectory and RIS phase shifts for enhancing the LoS link and achieving improved signal quality for GUs while transitioning between cells. The simulation results demonstrated that the proposed DDPG-based UAV-RIS framework achieved significant performance improvements. Specifically, the proposed scheme reduced HOF probability by up to 15% compared to a UAV-only deployment. The EE was improved by approximately 40% over baseline scenarios with increasing user densities. Throughput gains of 34.5% and 92.8% were observed compared to UAV-only and RIS-only systems, respectively, at a 300 m altitude. Additionally, the OP was reduced by up to 20% at higher RIS element configurations. These quantitative improvements validate the effectiveness of the proposed joint optimization approach for urban dense UAV-RIS deployments.  
Future WorkThe limited battery life of UAVs and the number of RIS elements can affect the communication performance of GUs in urban scenarios. Additionally, the computational complexity of the DDPG algorithm and reliance on UAVs may limit real-time operations and EE. Future work will address these challenges by optimizing UAV lifetime and the number of active RIS elements to improve EE and reduce HOF through strong LoS connectivity. Furthermore, we will explore multiple UAV-RIS systems and investigate the potential of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) to enhance GU mobility in dense urban areas.  
[PERSON] prepared the original draft of the manuscript and visualized the findings. [PERSON] and [PERSON] supervised the study, provided funding, and offered critical feedback on the manuscript. The manuscript was reviewed and edited by [PERSON], I.O.A., F.A., S.A., A.F.O. and F.Z.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported and funded by a Telekom Malaysia Research & Development (TMR&D) grant, RDTC/241134, MMUE/240095, Malaysia.  
All the data are available in the paper.  
The authors would like to acknowledge the Center for Wireless Technology, Faculty of Artificial Intelligence and Engineering, Multimedia University Cyberjaya, for providing essential resources, support, and facilities for this study. The authors declare no conflicts of interest.