## 4 Simulation Results
### Analysis on Terminal Density  
As shown in Figure 2, we study the effect of the terminal density \(\rho_{u}\) on the average system throughput. When the terminal density is fixed, the proposed method achieves a similar throughput performance to the inter-point method and outperforms the RDR-PA method and the uniform allocation method. The curves of the four methods follow a similar trend. The average system throughput increases and converges to a certain value for all methods as \(\rho_{u}\) increases. However, the growth rate of throughput decreases as \(\rho_{u}\) increases. When \(\rho_{u}\) is low, the proportion of APs that switch from a non-service status to a service status increases as \(\rho_{u}\) increases. Therefore, the average system throughput increases at a significant growth rate. When \(\rho_{u}\) reaches a certain threshold, almost all APs are in the service status. Due to the limited resources, the average system throughput converges as \(\rho_{u}\) increases. When the average system throughput begins to converge, the terminal density of the proposed algorithm is greater than that of the RDR-PA method and the uniform method, which indicates that the proposed algorithm has a higher resource utilization efficiency and supports higher terminal capacity compared to the other methods. When \(\rho_{u}\) is 0.89, the average throughput of the RDR-PA method is stable at 2.64 Gbps, while the average throughput of the proposed algorithm is stable at 4.88 Gbps. The performance improvement ratio is 84.8%, which indicates that the proposed algorithm is more suitable for high terminal density scenarios.  
In Figure 3, the average throughput per terminal is shown as a function of \(\rho_{u}\). Figure 3 indicates that an increase in the terminal density will lead to a decrease in the average throughput per terminal. This is because when \(\rho_{u}\) increases, the competition for resources among the terminals becomes more intense and the resource allocated to each terminal is relatively reduced. Compared with the RDR-PA method, the performance improvement ratio of the proposed algorithm increases as \(\rho_{u}\) increases. When \(\rho_{u}\) equals to 0.89, the performance improvement ratio is 85%.  
In Figure 4, the average ratio of satisfied terminals is shown as a function of \(\rho_{u}\When \(\rho_{u}\) is less than 0.22, the performance of the proposed algorithm is slightly lower than the RDR-PA method and the gap is within 5%. When \(\rho_{u}\) is greater than 0.22, the proposed algorithm outperforms the RDR-PA method and the performance improvement ratio increases as \(\rho_{u}\) increases. When \(\rho_{u}\) reaches 0.89, the performance improvement ratio reaches 67%. This is because when \(\rho_{u}\) is less than 0.22, the terminal density is relatively low, the competition among terminals is not intense and the resource is relatively abundant. When \(\rho_{u}\) is greater than 0.22, the competition is more intense as \(\rho_{u}\) increases and the advantage of the high resource utilization efficiency of the proposed algorithm is gradually highlighted. The simulation results show that the proposed algorithm has a better QoS guarantee performance in scenarios with high terminal density.