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Performance Analysis of an Intelligent Association Scheme in Ultra-Dense Networks Using Matern Cluster Process

机译:基于Matern聚类的超密集网络智能关联方案性能分析。

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Future cellular system is converging toward a heterogeneous network mixing Macro cells and high-density small cells clustering around hotspots. Meanwhile, in some hotspots, the density of active users may be inferior to the density of cells, which will bring a severe interference environment and cause waste of spectrum and energy. A more intelligent association scheme is needed to catch up with the dense reuse of spectrum. In this paper, we investigate the downlink association scheme of a user can be served by multiple small cells or only one Macro cell. Consequently, to capture the clustering feature, we model the distribution of random small cells with Matern Cluster Process (MCP) and Macro cells with Poisson Point Process (PPP). Under general assumptions, we derive the distribution properties of interference and based on that we derive an expression for the average downlink rate in two different serving modes. For efficient computation, we develop credible upper and lower bounds for its Laplace functional in different scheme. Then, we study the relation between average downlink rate and the main system parameters, namely: small cells density, clustering number and radius. Numerical simulation confirms the accuracy of analytical results. Our derived model can be used to guide the configuration of ultra-dense heterogeneous networks.
机译:未来的蜂窝系统正在朝着异构网络融合,该网络混合了宏小区和聚集在热点周围的高密度小小区。同时,在某些热点地区,活跃用户的密度可能低于小区的密度,这将带来严重的干扰环境,并造成频谱和能量的浪费。需要一种更智能的关联方案来赶上频谱的密集复用。在本文中,我们研究了一个用户的下行链路关联方案可以由多个小小区或仅一个宏小区服务。因此,为了捕获聚类特征,我们使用Matern聚类过程(MCP)和具有Poisson点过程(PPP)的宏单元对随机小单元格的分布进行建模。在一般假设下,我们得出干扰的分布特性,并基于此得出两种不同服务模式下平均下行链路速率的表达式。为了进行有效的计算,我们在不同的方案中为其Laplace函数开发了可靠的上限和下限。然后,我们研究了平均下行链路速率与主要系统参数之间的关系,即:小小区密度,聚类数和半径。数值模拟证实了分析结果的准确性。我们的派生模型可用于指导超密集异构网络的配置。

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