## 6 Simulation Results and Analysis
### Simulation Results and Analysis
However, at a 10 ms delay requirement, AIFG's average queue delay is reduced by 19.39% compared to AFG + HEFG and by 40.33% compared to IFG + HEFG and by 8.9% compared to DQN + HEFG. At a 15 ms delay requirement, AIFG's average queue delay decreases by 37.30% compared to AFG + HEFG and by 39.62% compared to IFG + HEFG and by 22.1% compared to DQN + HEFG. At a 20 ms delay requirement, AIFG's average queue delay is 43.90% lower than AFG + HEFG and 43.45% lower than IFG + HEFG and 30.3% lower than DQN + HEFG. When the delay requirement is low (e.g., 5 ms), the queue length typically does not exceed the AFG threshold, so the algorithm's performance is similar to that of AFG, and the advantages of AIFG are not fully manifested. However, when the delay requirement is high, AIFG dynamically optimizes the gap between frames, reducing delays and improving overall performance by adapting to changing conditions in the transmission environment. In this part, DQN + HEFG performs better than tra  
Figure 8: Multiplexing efficiency simulation results (delay-sensitive service queue). ditional rule-based approaches (AFG/IFG + HEFG) but does not reach the stability and optimization level of PPO-based AIFG. The simulation results validate the adaptability of the AIFG algorithm under varying delay requirements and the advantage of AIFG in reducing average queue delay. Based on the analysis of the above results, in the face of time-sensitive service queues, the AIFG algorithm proposed in this paper can effectively adjust the framing threshold according to the queue state. Compared with the traditional algorithm, it achieves a better multiplexing efficiency and a lower queuing delay while ensuring excellent throughput, which is an effective performance improvement. In terms of throughput performance, the AIFG algorithm has an average increase of 31.92% compared with the AFG + HEFG, IFG + HEFG and DQN + HEFG algorithms. In terms of the multiplexing efficiency, the AIFG algorithm has an average increase of 74.93% compared with the AFG + HEFG, IFG + HEFG and DQN + HEFG algorithms when the delay requirement is below 5 ms. In terms of the average queuing delay, the AIFG algorithm is 33.53% lower than the AFG + HEFG algorithm and 41.13% lower than the IFG + HEFG algorithm and 20.43% lower than the DQN + HEFG algorithm when the delay requirement is above 5 ms. In reinforcement learning applications, while DQN provides improvements over heuristic baselines, the PPO-based AIFG exhibits higher adaptability, stability, and better global optimization performance, making it more suitable for diverse and dynamic space network environments. To evaluate the scalability and robustness of the proposed AIFG algorithm under varying network sizes, we expand the simulation to assess performance across different network sizes, including 25-25, 50-50, 100-100, and 200-200 transmitter-receiver pairs. We maintain the same satellite relay architecture. These different simulation situations are all performed under the condition that the delay requirement is 20 ms. To further validate the scalability of the proposed AIFG algorithm, we specifically focus on the PPO training time, training memory consumption, convergence iterations, and resulting performance metrics such as delay, multiplexing efficiency, and throughput. The key performance metrics observed include the following:  
* Training computational overhead, measured as the cumulative time and training memory consumption required for PPO training to reach a stable policy. Figure 9: Average queue delay simulation results (delay-sensitive service queue). * Convergence time, quantified as the number of iterations needed for the reward function to stabilize within a defined tolerance range. As shown in Figure 10, the reward sum becomes negative in large-scale cases due to an increased queuing delay. In contrast, for smaller networks (25 and 50 nodes), the algorithm achieves convergence within fewer episodes and yields positive reward values.