## 5 Simulation Results  
This section presents simulations that evaluate dynamic topology characteristics and compare different routing metrics. Wireless channels adhere to the Rayleigh distribution of slow fading, with focus solely on the impact of network topology on capacity. In the simulation scenario, 30 nodes are randomly dispersed across a 1000 \(\times\) 1000 area. It should be noted that the simulations in this section are intended to evaluate link quality, and no physically meaningful units, such as m or m/s, are set for distance or speed. Nodes follow the proposed mobility model, generating velocities for their subsequent movements randomly. Figure 7a illustrates the initial positions of each node, while Figure 7b illustrates the positions at time 100 s. The x-axis and y-axis represent the coordinate positions of the nodes within the two-dimensional area (units are not defined). Figure 8 illustrates a comparison of the proposed method with conventional direct transmission in terms of topological performance under different coverage radii. The x-axis represents the size of the coverage radius, while the y-axis indicates the channel capacity performance. Statistical validation was carried out through 5000 Monte Carlo trials for each parameter configuration. The maximum speed is set to 1, and the path loss exponent is configured to 3.5. As can be seen from Figure 8, when the coverage radius is small, the opportunities for nodes in the network to establish communication links are limited, and the number of communicable adjacent nodes is restricted, resulting in poor network performance. As the coverage radius gradually increases, the channel capacity performance significantly improves. This is because more nodes can enter each other's communication range, increasing the number of links and thereby enhancing overall network performance. However, as the coverage radius continues to increase further, the performance begins to deteriorate. This is because, as the number of links  
Figure 6: Flowchart of selection. increases, interference also significantly rises. Under a large coverage radius, each node may experience signal interference from multiple other nodes, leading to a decline in network performance. Furthermore, it can be observed from the figure that the proposed method outperforms conventional direct transmission under almost all coverage radii. This optimization is attributed to the joint design of the transmission mode and link persistence estimation. In this experimental scenario, both methods achieve optimal performance when the coverage radius is 100, but the proposed method's performance is improved by 17% compared to direct transmission. This result indicates that there may exist an optimal coverage radius for such communication scenarios, where the number of links and interference are balanced. At this radius, each node can establish communication links with a sufficient number of other nodes, ensuring the existence of multiple available paths in the network while keeping the interference within an acceptable range, thus guaranteeing high channel capacity. Figure 9 shows the comparison curves between the estimated results and theoretical values of link persistence under simulation conditions of 30 nodes, communication radius R = 100, and path loss exponent \(\alpha\) = 3.5. The horizontal axis represents the maximum node mobility speed V\({}_{\max}\), while the vertical axis indicates the channel capacity performance. The green line represents the estimated results, and the purple line represents the theoretical  
Figure 8: Channel capacity performance. Figure 7: (a) Initial distribution of 30 nodes at t = 0 s; (b) distribution of 30 nodes after movement at t = 100 s.  
results. As can be seen from the figure, although there are deviations between the estimated and theoretical values due to the randomness of node mobility, their trends of change are highly consistent. This indicates that the proposed estimation method can capture the fundamental dynamic behavior of link persistence.