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The Ginibre Point Process as a Model for Wireless Networks With Repulsion

机译:Ginibre点过程作为具有排斥力的无线网络的模型

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摘要

The spatial structure of transmitters in wireless networks plays a key role in evaluating mutual interference and, hence, performance. Although the Poisson point process (PPP) has been widely used to model the spatial configuration of wireless networks, it is not suitable for networks with repulsion. The Ginibre point process (GPP) is one of the main examples of determinantal point processes that can be used to model random phenomena where repulsion is observed. Considering the accuracy, tractability, and practicability tradeoffs, we introduce and promote the -GPP, which is an intermediate class between the PPP and the GPP, as a model for wireless networks when the nodes exhibit repulsion. To show that the model leads to analytically tractable results in several cases of interest, we derive the mean and variance of the interference using two different approaches: the Palm measure approach and the reduced second-moment approach, and then provide approximations of the interference distribution by three known probability density functions. In addition, to show that the model is relevant for cellular systems, we derive the coverage probability of a typical user and find that the fitted -GPP can closely model the deployment of actual base stations in terms of coverage probability and other statistics.
机译:无线网络中发射机的空间结构在评估相互干扰并因此评估性能方面起着关键作用。尽管泊松点过程(PPP)已被广泛用于对无线网络的空间配置进行建模,但它不适用于具有排斥力的网络。 Ginibre点过程(GPP)是确定性点过程的主要示例之一,可用于对观察到排斥的随机现象进行建模。考虑到准确性,易处理性和实用性之间的折衷,我们引入并推广了-GPP,它是PPP和GPP之间的中间类,作为当节点表现出排斥力时的无线网络模型。为了表明该模型在几种感兴趣的情况下都能得出易于分析的结果,我们使用两种不同的方法(Palm量度方法和简化的第二矩方法)得出干扰的均值和方差,然后提供干扰分布的近似值通过三个已知的概率密度函数。另外,为了表明该模型与蜂窝系统相关,我们推导了典型用户的覆盖概率,并发现拟合的-GPP可以根据覆盖概率和其他统计数据对实际基站的部署进行紧密建模。

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