# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 5 Simulation and Analysis
### Simulation Results and Analysis  
First, we evaluated the backhaul capacity and handover frequency performance with diverse satellite and GS scales. The backhaul capacity is defined as the maximum flow of the network graph in each time slot. The handover frequency is equal to the average handover times of each GSs in an orbital period (around 6000 s). In these experiments, the weights of the utility function were set as \(w_{1}=0.64\), \(w_{2}=0.16\), and \(w_{3}=0.2\). Figure 5 proves the backhaul capacity and handover frequency performance with different constellation scales. The number of GSs is set to \(M=15\). The satellite number varies from 100 to 1500. As shown in Figure 4(a), the backhaul capacity increases with the increase in satellite number. MBCHS and MGCS outperform the MSTS in backhaul capacity. As the satellite number is less than 300, the performance of MBCHS is similar to that of MGCS. The reason is that only a few satellites can be considered in handover decisions as the constellation density is small. Thus, the handover results of these strategies are highly similar. As the satellite number increases from 300 to 1500, MBCHS outperforms MGCS, which indicates that the proposed handover strategy can effectively improve the backhaul capacity in a large-scale satellite network. As shown in Figure 4(b), the handover frequency of MBCHS is slightly higher than that of MSTS since MSTS focuses on minimizing the handover frequency. Meanwhile, the handover frequency of MBCHS is much lower than that of MGCS, which also aims at maximizing the network capacity. Figure 5: The performance comparison with different numbers of satellites. Figure 6 indicates the backhaul capacity and handover frequency performance with different numbers of GSs varying from 5 to 20. The number of satellites is set to \(N=800\). As shown in Figure 5(a), the backhaul capacity of the three schemes increases with the increase in GS number because the number of feeder links for the backhaul service increases. Obviously, MBCHS outperforms the benchmarks in backhaul capacity while the handover frequency is moderate. As shown in Figure 5(b), the handover frequency of MBCHS is between these two benchmarks. Meanwhile, the handover frequency of MBCHS gradually approaches MSTS while the GS number increases. To explore the optimal weight setting of the utility function, we also evaluated the performance with different weights of the handover control factor and service time factor. Figure 7 shows the simulation results with \(w_{3}\), while \(w_{1}=0.8*(1-w_{3})\) and \(w_{2}=0.2*(1-w_{3})\). As the weight varies from 0 to 0.2, the handover frequency drops rapidly while the backhaul capacity changes slightly. The backhaul capacity also drops rapidly as the weight varies from 0.2 to 0.4. When the weight is greater than 0.4, the backhaul capacity and handover frequency vary slightly. Thus, selecting a small weight for the handover control factor can significantly reduce the handover frequency while ensuring the backhaul capacity performance. Figure 6: The performance comparison with different numbers of GSs. Figure 8 shows the simulation results with different weights of service time factor while \(w_{1}=0.8*(1-w_{2})\) and \(w_{3}=0.2*(1-w_{2})\). As shown in Figure 7(a), a small weight can significantly reduce the handover frequency. The reason is that there are many visible satellites with similar weights in the handover decision-making process. Thus, the service time factor can also play an important role, even if it is small. As the weight varies from 0 to 0.4, the backhaul capacity and handover frequency both change slightly. When the weight is greater than 0.6, the backhaul capacity drops rapidly while the handover frequency increases rapidly. The reason is that each feeder link tends to switch to another satellite with a larger service time at each time slot when the weight is greater than 0.6. These satellites with large service times are located at the margin of the GS's coverage area and have poor channel quality.