# 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
### Performance Trade-Offs and Design Implications

The integration of RISs with NTNs introduces critical trade-offs between performance metrics. These trade-offs are rooted in the mathematical relationships governing the system model and are evident in the simulation results. The analysis presented in Tables 3 and 4 provides a detailed evaluation of the performance metrics and trade-offs for RIS-assisted 6G-enabled drone communication systems and other high-mobility NTN scenarios. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Trade-Off** & **Key Observation** & **Optimal Design Choice** \\ \hline SINR vs. Energy Efficiency & SINR saturates at \(M=30\); energy efficiency peaks at \(M=30\). & \(M=30\) (peak efficiency). \\ \hline Throughput vs. Latency & Throughput grows logarithmically; latency increases linearly. & \(M=30\)–\(40\) (balance for real-time systems). \\ \hline Outage vs. Complexity & Outage drops exponentially; complexity increases quadratically. & \(M=40\)–\(50\) for reliability, \(M=20\)–\(30\) for low complexity. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of trade-offs. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **SINR vs. Energy Efficiency** & **Throughput vs. Latency** & **Outage Probability vs. Complexity** \\ \hline \multirow{7}{*}{Observation} & **SIN (dB):** Urban scenarios exhibit a stable yet slightly declining SINR beyond \(M=30\). Highway scenarios show steady improvement in SINR with increasing \(M\), while rural and emergency scenarios face SINR degradation due to interference. Indoor scenarios maintain a stable SINR irrespective of \(M\). & **Throughput (Mbps):** Throughput improves across most scenarios with \(M\), peaking in rural and indoor scenarios at higher \(M\). Highway scenarios show diminishing returns after an initial rise, and emergency scenarios decline slightly with larger \(M\). Urban throughput grows steadily but modestly. & **Outage Probability:** Urban scenarios see steady improvement in outage probability as \(M\) increases. \\ \cline{3-4}  & **SINR (dB):** Urban exhibits a stable yet slightly declining SINR irrespective of \(M\). & **Throughput (Mbps):** Throughput improves across most scenarios with \(M\), peaking in rural and indoor scenarios at higher \(M\). Highway scenarios show \(M\). Urban scenarios decline in energy efficiency at higher \(M\) due to increased power consumption. & **Outage Probability:** Urban scenarios see steady improvement in outage probability as \(M\) increases. \\ \hline \multirow{7}{*}{Trade-Off} & **SINR (dB):** Urban exhibits a stable yet slightly declining SINR irrespective of \(M\). & **Throughput (Mbps):** Throughput improves across most scenarios



hand, prioritize the extension of coverage, making a larger \(M\) (e.g., \(M=50\)) more justifiable despite the increase in power consumption and diminishing returns in SINR and throughput beyond \(M=30\). This highlights the importance of scenario-specific tuning for optimal performance. A key takeaway is the diminishing beamforming gains and increasing power consumption observed at higher RIS element counts, particularly in emergency scenarios, where energy efficiency starts declining at \(M>30\). The balance between SINR, energy efficiency, and environmental constraints further underscores the need for adaptive RIS deployment strategies tailored to specific environments. Another critical trade-off identified is between the probability of outages and computational complexity. Mission critical systems, such as emergency networks, require \(M=40\)-\(50\) to achieve ultra-reliability (\(P_{\text{out}}<1\%\)). However, this increase in \(M\) significantly increases computational complexity, as RIS reconfiguration times increase quadratically with \(M\) (for example, approximately \(50\) ms for \(M=50\)).