## 6 Simulation Results and Analysis
### Simulation Results and Analysis
From Figure 7, it can be seen that the throughput of the AIFG algorithm is generally higher compared to other frame generation algorithms. When the delay requirement is 5 ms, the throughput of the AIFG algorithm is 466 Kbps, which is an increase of 78.05% over both the AFG + HEFG and IFG + HEFG algorithms. With a delay requirement of 10 ms, the throughput of AIFG is 464 Kbps, which is an increase of 25.37% compared to that of AFG + HEFG and IFG + HEFG. When the delay requirement is 15 ms, the AIFG algorithm achieves the maximum throughput of 606.456 Kbps, which is 28.66% higher than that of the AFG + HEFG and IFG + HEFG algorithms. At a delay requirement of 20 ms, the throughput of AIFG also improves, with an increase of 17.18% compared to both AFG + HEFG and IFG + HEFG. The AIFG consistently exhibits superior throughput, especially under 5 ms and 10 ms delay thresholds, where it outperforms DQN + HEFG by margin of 49.9% and 12.7% respectively. Under 15 ms and 20 ms delay thresholds, the throughput of AIFG is 15.8% and 6.1% higher than that of DQN + HEFG, respectively. When the delay requirement is low, AIFG shows a higher throughput improvement rate, and its performance change is optimal. Conversely, when the delay requirement is high, AIFG is able to achieve a larger throughput value. This highlights the effectiveness of PPO in guiding reward shaping and decision making over longer time horizons. Due to the FIFO transmission mechanism, the queue's multiplexing efficiency is always 100%. As shown in Figure 8, the multiplexing efficiency of the AIFG algorithm is higher compared to the IFG + HEFG, AFG + HEFG and DQN + HEFG algorithms under smaller delay requirements. With a delay requirement of 5 ms, AIFG's multiplexing efficiency is 69.25%, which is an increase of 92.36% over both AFG + HEFG and IFG + HEFG, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \(\mathbf{\kappa}\) & \(\mathbf{\beta}\) & \(\mathbf{\gamma}\) & \(\mathbf{\delta}\) \\ \hline
5 ms & 0.9 & 0.5 & 0.8 & 1.50 \\
10 ms & 0.8 & 0.2 & 0.5 & 1.10 \\
15 ms & 0.6 & 0.4 & 0.5 & 0.90 \\
20 ms & 0.8 & 0.2 & 0.4 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Reward function parameters (different delay threshold requirements). Figure 7: Throughput simulation results (delay-sensitive service queue). and an increase of 63.2% over DQN + HEFG. Under a delay requirement of 10 ms, the multiplexing efficiency of the AIFG algorithm is 69.1%, which is 52.47% higher than that of the AFG + HEFG and IFG + HEFG algorithms, and an increase of 35.5% over DQN + HEFG. At a 15 ms delay requirement, AIFG's multiplexing efficiency is 12.26% higher than that of AFG + HEFG and IFG + HEFG, while it is only 3.2% higher than that of DQN + HEFG. When the delay requirement is 20 ms, the multiplexing efficiency of AIFG reaches its maximum value of 94.7%, with an increase of 1.4% compared to DQN + HEFG, but with a slight decrease compared to AFG + HEFG and IFG + HEFG. When the delay requirement is high, the multiplexing efficiency improvement rate of AIFG decreases. This is constrained by the time threshold and influenced by the fixed AOS transmission frame size. Nevertheless, the overall trend in multiplexing efficiency remains upward. Among learning-based methods, AIFG shows better multiplexing efficiency than DQN + HEFG, especially under strict delay conditions (5 ms, 10 ms), indicating a more refined policy learned through PPO. Figure 9 shows that AIFG consistently exhibits the lowest delay across all time requirements, outperforming all other methods. This is especially noticeable under higher time requirements (15 ms and 20 ms), where the delay is substantially smaller. Meanwhile, the FIFO-based method exhibits slower processing times due to its inherent characteristics. With a delay requirement of 5 ms, the average queue delay for AIFG, DQN + HEFG, AFG + HEFG, and IFG + HEFG is almost the same, ranging from 6 to 7 ms.