## 5 Simulation and Results
### Performance of Our Proposed Algorithm
Figure 10: Access success rate performance with different algorithms. Figure 9: Average delay performance with different algorithms. As the traffic demand increases, the access success rate in the entire beam hopping period gradually decreases, but in general, the access success rate of the scheme using the genetic algorithm is higher than that of the polling scheme. This is because the order of serving cells in the polling scheme is relatively fixed, and it is impossible to schedule beams flexibly according to real-time demand, which will inevitably cause many data packets to be abandoned due to waiting for too long, and the access success rate will decrease accordingly. Our method performs better than GAWIC-BH, which is reflected in the fact that the access success rate decreases more smoothly. The access success rate of Random-BH is the lowest, and as the traffic intensity increases, the access success rate drops significantly. The illumination pattern selection based on weighted greedy strategy ensures the service fairness of each cell. On the one hand, the weight of the amount allows the cells with more demand to be preferentially served, avoiding congestion caused by excessive demand that cannot be satisfied in time. On the other hand, the urgency weight is set, even if the demand in the cell is not high, and if it is not served for a long time, the urgency of the data packets in the cell will increase greatly, and the beam will be preferentially scheduled in the subsequent time to avoid access failure. Under the scenario of uneven distribution of terrestrial traffic demand, several comparison simulations are carried out to show the superiority of our proposed algorithm. Twenty hotspot areas are set in 195 cells to simulate the performance of each algorithm for scheduling beam hopping resources under different traffic intensities. The traffic intensity is set as follows: the traffic demand in the hotspot cells is 10 times that of the non-hotspot cells. The sum of the traffic of all cells is the same as that in the even distribution scenario, corresponding to the traffic intensity from 400 to 700 packets in the even scenarios. The simulation results are shown in Figure 12, and the data comparison between the two scenarios is shown in Tables 4 and 5. As depicted in the above figures and tables, our proposed algorithm outperforms all the comparison algorithms on all the performance metrics. The performance curves of various algorithms are similar to those in the even distribution scenario. It is not difficult to observe that when the traffic distribution is uneven, our algorithm and GAWIC-BH can also achieve relatively high throughput and access success rate while the performance of Random-BH and Polling-BH deteriorated significantly due to inflexibility. Additionally, the average delay obtained by our mechanism and GAWIC-BH is even shorter than that in even  
Figure 11: Fairness performance with different algorithms. distribution scenario when traffic demand is relatively small, indicating that the genetic algorithm is very suitable for adapting to uneven space-time distribution of demands. Meanwhile, the advantages of our proposed scheduling scheme in ensuring service fairness between cells are very prominent. When the traffic intensity increases, the advantages of our algorithm compared with GAWIC-BH gradually become obvious and it can achieve better performance with only \(1/4\) the number of iterations of GAWIC-BH.