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A Geometrical Probability Approach to Location-Critical Network Performance Metrics.

机译:关键位置网络性能指标的几何概率方法。

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摘要

The field of wireless communications has been experiencing tremendous growth with the ever-increasing dependence on wireless services. In the operation of a communication network, the network coverage and node placement are of profound importance. The network performance metrics can be modeled as nonlinear functions of inter-node distances. Therefore, a geometric abstraction of the distance between wireless devices becomes a prerequisite for accurate system modeling and analysis. A geometrical probability approach is presented in this dissertation to characterize the probabilistic distance properties, for analyzing the location-critical performance metrics through various spatial distance distributions.;Ideally, the research in geometrical probability shall give results for the distance distributions 1) over elementary geometries such as a straight line, squares and rectangles, and 2) over complex geometries such as rhombuses and hexagons. Both 1) and 2) are the representative topological shapes for communication networks. The current probability and statistics literature has explicit results for 1), whereas the results for 2) are not in existence. In particular, the absence of the distance distributions for rhombuses and hexagons has posed challenges towards the analytical modeling of location-critical performance metrics in complex geometries. This dissertation is dedicated to the application of existing results in 1) elementary geometries to the networking area, and the development of a new approach to deriving the distance distributions for complex geometries in 2), bridging the gap between the geometrical probability and networking research.;The contribution of this dissertation is twofold. First, the one-dimensional Poisson point process in 1) is applied to the message dissemination in vehicular ad-hoc networks, where the network geometry is constrained by highways and city blocks. Second, a new approach is developed to derive the closed-form distributions of inter-node distances associated with rhombuses and hexagons in 2), which are obtained for the first time in the literature. Analytical models can be constructed for characterizing the location-critical network performance metrics, such as connectivity, nearest/farthest neighbor, transmission power, and path loss in wireless networks. Through both analytical and simulation results, this dissertation demonstrates that this geometrical probability approach provides accurate information essential to successful network protocol and system design, and goes beyond the approximations or Monte Carlo simulations by gracefully eliminating the empirical errors.
机译:随着对无线服务的依赖性越来越高,无线通信领域正在经历巨大的增长。在通信网络的操作中,网络覆盖范围和节点放置极为重要。可以将网络性能指标建模为节点间距离的非线性函数。因此,无线设备之间距离的几何抽象成为精确系统建模和分析的先决条件。本文提出了一种几何概率方法来表征概率距离特性,以通过各种空间距离分布来分析位置关键性能指标。理想情况下,几何概率的研究应给出基本几何上距离分布的结果1)例如直线,正方形和矩形,以及2)复杂几何形状(例如菱形和六边形)。 1)和2)都是通信网络的代表性拓扑形状。当前的概率和统计文献对1)有明确的结果,而对于2)的结果不存在。尤其是,缺少菱形和六角形的距离分布对复杂几何中的位置关键性能指标的分析建模提出了挑战。本论文致力于将现有结果在1)基本几何中的应用到网络领域,并开发一种新的方法来推导复杂几何中的距离分布(2),弥合几何概率与网络研究之间的差距。 ;本论文的贡献是双重的。首先,将1)中的一维Poisson点过程应用于车辆自组织网络中的消息分发,该网络的几何结构受高速公路和城市街区的约束。其次,开发了一种新方法来导出与2)中菱形和六边形相关的节点间距离的闭合形式分布,这是文献中首次获得的。可以构建分析模型来表征位置关键型网络性能指标,例如连接性,最近/最远的邻居,传输功率以及无线网络中的路径损耗。通过分析和仿真结果,本文证明了这种几何概率方法提供了成功的网络协议和系统设计必不可少的准确信息,并且通过优雅地消除了经验误差而超越了近似或蒙特卡洛仿真。

著录项

  • 作者

    Zhuang, Yanyan.;

  • 作者单位

    University of Victoria (Canada).;

  • 授予单位 University of Victoria (Canada).;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 172 p.
  • 总页数 172
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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