# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Performance Evaluation and Trade-Off Analysis
#### 4.3.4 Latency
For instance, while increasing the number of RIS elements (\(M\)) improves signal quality (e.g., SINR rising to 15 dB in urban environments) and reduces outage probability (to <1% for \(M=50\)), it introduces trade-offs such as higher energy consumption (e.g., a 20% efficiency drop beyond \(M=30\)), increased latency (up to 0.05 ms for \(M=50\)), and quadratic growth in computational complexity. These findings underscore the necessity of scenario-specific adaptive configurations: urban interference-heavy environments favor a smaller \(M\) (e.g., \(M=20\)) to optimize energy efficiency, while rural areas benefit from a larger \(M\) (e.g., \(M=50\)) to maximize coverage and reliability. Similarly, mission-critical applications like emergency networks prioritize ultra-reliability (\(P_{\text{out}}<1\%\)) at the cost of complexity, whereas latency-sensitive systems such as autonomous drones require lightweight RIS configurations (\(M\leq 30\)) to meet real-time demands. Future research will focus on real-time optimization of RIS-assisted NTNs using advanced machine learning techniques such as deep reinforcement learning and federated learning. The integration of RIS with IoT and smart city infrastructure will be explored to enhance connectivity in diverse environments. Channel modeling for mmWave/THz bands and 3D mobility will support more accurate performance analysis. In addition, hardware prototyping and standardization efforts will ensure scalable, energy-efficient, and interoperable RIS-NTN systems. The future development of RIS-assisted UAV networks can greatly benefit from the integration of intelligent multimodal sensing and communication frameworks. Emerging techniques, such as digital twins in real time [34] and LiDAR-assisted channel modeling [35] offer the potential to improve environmental awareness, user localization, and obstacle mapping, which are crucial for dynamic aerial deployments. By combining LiDAR sensing, GPS data, and visual sensors, UAVs can proactively detect changes in the propagation environment, enabling the RIS to adapt its phase shifts more effectively in real-time. Furthermore, multimodal sensing can support more accurate CSI prediction and trajectory-aware RIS configurations, thus mitigating the negative effects of Doppler shifts and fast fading in high-mobility scenarios. Incorporating these sensing-communication integrations into our proposed optimization framework would allow future aerial networks to achieve enhanced reliability, lower outage probability, and better support for URLLC applications. Conceptualization, M.S.A., M.S. and I.K. ; methodology, M.S.A. and I.K. ; software, M.S.A., M.S. and I.K. ; validation, M.S. and I.K. ; formal analysis, M.S.A. and M.S. ; investigation, M.S.A. and I.K. ; resources, M.S. and I.K. ; data curation, M.S.A., M.S. and I.K. ; writing--original draft preparation, M.S.A. and M.S. ; writing--review and editing, M.S.A., M.S. and I.K. ; visualization, M.S.A. and I.K. ; supervision, M.S. and I.K. ; project administration, M.S. and I.K. ; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript. **Funding:** We acknowledge the support of the BK21 project, South Korea. The work of [PERSON] was supported by the 6G-SENSES project of the Smart Networks and Services Joint Undertaking (SNS JU) under the Horizon Europe research and innovation program of the European Union under Grant Agreement No. 101139282. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Conflicts of Interest:** The authors declare no conflicts of interest. ## References

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