# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 4 Presentation and Analysis of Simulation Results
### Comparison of Proposed and Traditional Algorithms in the Strongest Interference Scene  
In this simulation, we constructed a different traffic model with the strongest interference to test the proposed and traditional algorithms performance.  
We constructed the traffic distribution with the strongest interference so that the hot spot area and non-hot spot area are alternated to examine the proposed and traditional algorithms' performance. The distribution is shown in Figure 10.  
Figure 9: The distribution of delay for all delay users.  
Figure 11 illustrates the comparison of system performance between the proposed algorithm and the traditional algorithm under the distribution of strong potential interference services. It can be observed that the system throughput and total system delay of the system based on the proposed algorithm outperform those of the traditional algorithm. Specifically, compared to the traditional algorithm with a delay sensitivity factor of 2, the proposed algorithm achieves a 1.43% increase in total system throughput and a 62.25% improvement in system delay optimization. This is because the proposed algorithm reasonably utilizes the broad coverage characteristics of large beams to serve ground users frequently, significantly reducing ground user delays. From the user delay distribution shown in Figure 12, it can be clearly seen that after adopting the proposed algorithm, the number of low-delay users is much higher than that of the traditional algorithm, while the number of high-delay users is much lower than that of the traditional algorithm.  
In addition, this simulation examines the system performance of the traditional algorithm using different delay sensitivity factors. It can be observed that, similar to Figure 9, the system performance of the traditional algorithm still follows the trend of smaller delays and lower throughput as the delay factor increases. However, when the delay sensitivity factor reaches a certain level, the system throughput decreases rapidly, while the reduction in total system delay is not significant. This is because the system is already close to the minimum delay, so increasing the delay sensitivity factor to serve more non-hotspot area users results in a limited reduction in delay. Moreover, due to the interlaced distribution of non-hotspot and hotspot areas in the set scenario, the influence between adjacent cells be-comes stronger. Adopting a larger delay sensitivity factor will reduce the service time for more hotspot area users around the cells, leading to a significant decrease in throughput, resulting in a cliff-like deterioration in throughput when the delay sensitivity factor equals 5.  
Figure 10: The constructed traffic distribution.  
From Figure 12, it can be observed that, similar to Figure 9, increasing the user delay sensitivity factor results in a decrease in the number of high-delay users. However, unlike Figure 9, there is no noticeable reduction in the number of low-delay users here. Therefore, increasing the user delay sensitivity factor at this point will not lead to system delay deterioration as seen in Figure 9, but it will also result in a decrease in system throughput.  
Figure 11: The comparison of total delay and throughput between proposed and traditional algorithms in the new scenario.