## 6 Simulation Results and Analysis
### Simulation Results and Analysis
Notably, the algorithm's accumulated reward converges well even in large-scale environments. As summarized in Table 5, the training time increases with the node scale, reaching approximately 798 s in the 200-node configuration, compared to just 41.5 s for 50 nodes. Similarly, the number of PPO training iterations required for reward convergence increases from 40 to 80. Nonetheless, AIFG still delivers high efficiency and throughput across all scenarios. Although the computational cost increases as the network grows, the performance in delay remains acceptable. These results, summarized in Figure 10 and Table 5, demonstrate that AIFG remains computationally viable and robust under scale expansion, offering consistent performance and QoS adaptability even in dense satellite constellations. In summary, the AIFG algorithm demonstrates strong scalability with a predictable computational cost growth and robust convergence behavior, indicating its feasibility in large-scale deployment. To further investigate the robustness and adaptability of the proposed AIFG algorithm under varying network configurations, two additional simulation scenarios were  
Figure 10: Reward convergence curves under different network scales (delay requirement: 20 ms). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network Size** & **PPO Training** & \multicolumn{2}{c}{**PPO Training**} & \multicolumn{2}{c}{**Convergence**} & \multicolumn{2}{c}{**Average Delay**} & \multicolumn{2}{c}{**Multiplexing**} & \multicolumn{2}{c}{**Throughput**} \\ **(T-R Pairs)** & **Time (s)** & **Consumption** & **(MB)** & **Iterations** & **(ms)** & **Efficiency (\%)** & **(Kbps)** \\ \hline
25–25 Nodes & 98.52 & 260.64 & 45 & 8.64923 & 86.3845 & 389.417 \\
50–50 Nodes & 41.46 & 261.61 & 40 & 10.28250 & 98.6160 & 554.862 \\
100–100 Nodes & 365.48 & 261.09 & 68 & 52.43700 & 95.4074 & 643.059 \\
200–200 Nodes & 797.71 & 259.97 & 80 & 70.74900 & 95.4598 & 643.448 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics and computational overhead of AIFG under different network scales (delay requirement: 20 ms). conducted. The first scenario explores the influence of different MPDU sizes on system performance. Specifically, MPDU sizes were varied across 800, 1200, 1500 (default), and 1800 bytes. As shown in Table 6, the AIFG algorithm maintains consistently high throughput and multiplexing efficiency across different MPDU sizes. The results show that while increasing MPDU size improves multiplexing efficiency up to a point, overly large frame sizes (e.g., 1800 bytes) lead to degraded throughput, likely due to transmission and fragmentation inefficiencies. The default size of 1500 bytes strikes a good balance among the average delay (10.77 ms), efficiency (99.49%), and throughput (566.45 Kbps), confirming the appropriateness of the default setting. Interestingly, even when reducing the frame size to 800 bytes, the algorithm achieves strong performance with an average delay of only 7.23 ms and a throughput of 559.61 Kbps. These results indicate that the AIFG algorithm is capable of dynamically adjusting the frame threshold to maintain high performance, even under less efficient framing conditions. In the second scenario, the satellite link bandwidth was adjusted across 3, 6 (default), 9, 15, and 20 Mbps to simulate the variability caused by orbital distance, channel fading, or dynamic scheduling. As summarized in Table 7, the AIFG algorithm continues to demonstrate resilient performance, with stable average delay and throughput across all bandwidth conditions. Although slight improvements in throughput are observed as bandwidth increases, the average delay and efficiency remain within a narrow and acceptable range. The default configuration of 6 Mbps achieved the highest throughput at 575.18 Kbps and the best multiplexing efficiency at 99.52%. Even in constrained link scenarios (e.g., 3 Mbps), the algorithm sustains efficient operation, with a throughput of 566.86 Kbps.