来自当前移动蜂窝网络的基站:测量、空间建模和分析

Jiaxin Zhang, Wenbo Wang, Xing Zhang, Yu Huang, Zhuowen Su, Zongchao Liu
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引用次数: 17

摘要

在异构蜂窝网络中,基站的空间特性对系统性能影响很大。现有的二维六边形网格模型或均匀空间泊松点过程(SPPP)等模型都是基于理想或均匀分布的假设,但热点用户的聚集行为对低功率节点(lpn)的位置有重要影响,因此这些模型无法表征当前移动蜂窝网络中BSs的分布。本文首先对现有的空间模型进行了分析。然后,基于中国某大城市移动运营商的真实数据,提出了一套基于密集城市、城市和郊区三个典型区域的空间模型。对于人口密集的城市地区,提出了“两层泊松聚类叠加过程”来模拟现实世界BSs的空间特征。具体而言,对于城市和郊区,仍然可以使用传统的SPPP模型。最后,对用户行为与BS分布之间的基本关系进行了说明和总结。大量研究结果表明,SPPP仅适用于用户不明显聚集的城市和郊区。这些模型的主要参数为理论分析和计算机仿真提供了参考,更合理地描述了复杂的空间结构,更准确地反映了当前移动蜂窝网络的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Base stations from current mobile cellular networks: Measurement, spatial modeling and analysis
In heterogeneous cellular networks spatial characteristics of base stations (BSs) influence the system performance intensively. Existing models like two-dimensional hexagonal grid model or homogeneous spatial poisson point process (SPPP) are based on the assumption that BSs are ideal or uniformly distributed, but the aggregation behavior of users in hot spots has an important effect on the location of low power nodes (LPNs), so these models fail to characterize the distribution of BSs in the current mobile cellular networks. In this paper, firstly existing spatial models are analyzed. Then, based on real data from a mobile operator in one large city of China, a set of spatial models is proposed in three typical regions: dense urban, urban and suburban. For dense urban area, “Two Tiers Poisson Cluster Superimposed Process” is proposed to model the spatial characteristics of real-world BSs. Specifically, for urban and suburban area, conventional SPPP model still can be used. Finally, the fundamental relationship between user behavior and BS distribution is illustrated and summarized. Numerous results show that SPPP is only appropriate in the urban and suburban regions where users are not gathered together obviously. Principal parameters of these models are provided as reference for the theoretical analysis and computer simulation, which describe the complex spatial configuration more reasonably and reflect the current mobile cellular network performance more precisely.
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