## 6 Simulation Results and Analysis
### Simulation Results and Analysis  
Under different delay requirements, the AOS automatically adjusts and produces varying network performance results, leading to changes in parameters such as the final threshold values, delay, multiplexing efficiency, and throughput. To achieve more accurate results from the advanced intelligent frame generation algorithm, the simulation sets four different delay requirements \(T_{h}\): 5 ms, 10 ms, 15 ms, and 20 ms. At the same time, according to the delay requirements, iteration parameters, reward function parameters, and final reward values of the old and new policy are flexibly adjusted to train the agent to obtain a more optimized delay threshold. In the simulation of this algorithm, the change in the delay requirement combined with adjustments to the reward function parameters can guide the correct training and learning of the agent, which makes the agent complete. Then, the agent is able to make the optimal policy. The difference in the four parameter settings in the reward function will greatly affect the performance of the algorithm. Therefore, the numerical selection of the parameter group is particularly important. This simulation takes the setting delay requirement of 5 ms as an example. Multiple parameter sets of reward functions are as shown in Table 3. For each case, the reward function, throughput, average delay, and multiplexing efficiency are observed. The optimal parameter group value is obtained via a comparison, and the reward value convergence curve changes as shown in Figure 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Set Number** & \(\alpha\) & \(\beta\) & \(\gamma\) & \(\delta\) \\ \hline
1 & 0.7 & 0.3 & 0.2 & 1.20 \\
2 & 0.4 & 0.6 & 0.4 & 0.75 \\
3 & 0.9 & 0.5 & 0.8 & 1.50 \\
4 & 0.5 & 0.5 & 0.5 & 1.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reward function parameters (delay requirement: 5 ms). Figure 5: Comparison of reward convergence curves for four parameter settings (delay requirement: 5 ms). Different parameter settings will bring different changes in the value of the reward function. Figure 5 clearly shows the mainstream trend of the reward function, which gradually rises in the early stage of training. When the number of iterations is about 40, it begins to stabilize and enters the platform period, indicating the convergence of the reward function. Among the four parameter groups selected, the growth rate of the convergence function is the fastest in the case of Parameter Group 3, and the growth rate of the Parameter Group 2 is slower. At the same time, the convergence effect of Parameter Groups 1, 3 and 4 is better, but the convergence effect of Parameter Group 2 is poor. From Figure 6, it can be observed that the average delay and throughput are optimal for Parameter Group 3, while the multiplexing efficiency is highest for Parameter Group 4. Overall, selecting Parameter Group 3 for AIFG algorithm training is the most optimal choice. For the four different network processing strategies, different simulation performances are observed under varying delay threshold requirements. Except for the FIFO + FIFO algorithm, which cannot accurately set delay requirements, the other strategies produce a single result under each delay requirement. In this simulation, the parameter groups of the optimal reward function are set for the four delay requirements, and the specific values are shown in Table 4. These sets determine the weights for delay and efficiency trade-offs in the agent's learning process. The detailed values of \(\alpha\), \(\beta\), \(\gamma\), and \(\delta\) for each delay condition are summarized in Table 4, covering thresholds of 5 ms, 10 ms, 15 ms, and 20 ms, respectively. Figure 6: Simulation Performance comparison of AIFG algorithm for four parameter settings (delay requirement: 5 ms). The following series of figures provides the frame generation delay, throughput, and multiplexing efficiency for the four strategies under different delay requirements.