# 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
Although these systems can tolerate the additional complexity due to their need for reliability, simpler networks with \(M=20\)-\(30\) achieve a better balance between the probability of outage and the computational overhead. However, they still experience higher outage probabilities (10-15%) in environments with unpredictable interference or limited propagation conditions, such as rural or emergency scenarios. For real-time applications sensitive to latency, such as drone communication or autonomous vehicular networks, urban and emergency scenarios benefit from \(M\leq 30\), which ensures reduced latency and maintains computational efficiency. In contrast, bandwidth-intensive applications, such as video streaming in rural or highway scenarios, can tolerate slightly higher latency in exchange for maximizing throughput at \(M=50\). In practical deployments, maximizing the SINR typically requires increasing \(M\) (the number of RIS elements) to improve beamforming gain. However, this introduces critical trade-offs: power consumption scales with \(M\) due to per-element requirements, while processing latency increases with real-time phase-shift computations, both adversely impacting energy efficiency and system responsiveness. Furthermore, environmental constraints, including obstacle density, user mobility patterns, signal blockage, and interference levels, fundamentally constrain the marginal utility of additional elements. For example, in urban environments with severe multipath propagation and dense interference, large RIS arrays exhibit diminishing returns due to constructive-destructive interference phenomena. In contrast, in rural/highway settings with dominant LoS paths, gains scale more linearly with \(M\). Consequently, adaptive deployment strategies are essential, requiring dynamic joint optimization of the following based on real-time measurements and environmental classification:

1. RIS element count (\(M\));
2. Physical placement geometry;
3. Phase-shift update algorithms. For latency-sensitive applications (e.g., emergency response, autonomous navigation), smaller arrays with computationally efficient heuristics prove optimal. Conversely, coverage-oriented deployments benefit from larger arrays employing offline-trained predictive optimization. This context-aware adaptation ensures Pareto-optimal balancing of the SINR, energy efficiency, and latency under practical constraints. Overall, the analysis emphasizes the critical need for environment-aware RIS tuning and algorithmic approaches (e.g., gradient-based optimization for smaller \(M\) versus machine learning models for higher \(M\)) to address the performance trade-offs in diverse NTN and high-mobility scenarios. These findings underscore the importance of adaptive RIS configurations, where dynamic tuning of \(M\) and phase shifts is essential to address varying environmental conditions and application priorities. Furthermore, algorithm se lection plays a crucial role, with a smaller \(M\) enabling gradient-based optimization and larger \(M\) necessitating machine learning for real-time operation. However, the results highlight the potential of RIS-assisted NTNs to enable ultra-reliable, low-latency communication in 6G networks, particularly for high-mobility use cases like drone swarms and autonomous systems. ## 6 Conclusions and Future Directions

This article investigates the integration of RISs with NTNs to address critical connectivity challenges in high-mobility scenarios, such as autonomous vehicles, drone swarms, and aerial networks. Our results demonstrate that RIS-assisted NTNs significantly enhance key performance metrics such as the SINR, throughput, and reliability in dynamic environments; however, their effectiveness depends on achieving an optimal trade-off among interdependent and often competing parameters.