论文标题
评估基于随机几何模型的LEO卫星网络分析的准确性
Evaluating the Accuracy of Stochastic Geometry Based Models for LEO Satellite Networks Analysis
论文作者
论文摘要
本文研究了基于低地球轨道（LEO）卫星网络的最近提出的基于随机几何建模的准确性。特别是，我们使用瓦斯坦距离启发的方法来分析包括斐波那契晶格和轨道模型在内的不同模型之间的距离。我们提出了一种算法来计算生成点集之间的距离。接下来，我们使用数值结果来测试算法的性能，并分析随机几何模型与其他更广泛可接受的模型之间的距离。
This paper investigates the accuracy of recently proposed stochastic geometry-based modeling of low earth orbit (LEO) satellite networks. In particular, we use the Wasserstein Distance-inspired method to analyze the distances between different models, including Fibonacci lattice and orbit models. We propose an algorithm to calculate the distance between the generated point sets. Next, we test the algorithm's performance and analyze the distance between the stochastic geometry model and other more widely acceptable models using numerical results.
