New insights into the stochastic geometry analysis of dense CSMA networks
New insights into the stochastic geometry analysis of dense CSMA networks
复制标题
DOI：
10.1109/infcom.2011.5935092
复制
发表时间：
2011-04
期刊：
影响因子：
--
通讯作者：
G. Alfano;M. Garetto;Emilio Leonardi
中科院分区：
文献类型：
--
作者：
G. Alfano;M. Garetto;Emilio Leonardi
事实证明，随机几何是采用随机MAC协议（例如ALOHA和CSMA）的密集无线网络建模的强大工具。该方法的主要优势在于其能够考虑节点位置的随机性以及基于SINR在物理层的准确描述，这允许考虑每个链路上的随机衰落。采用随机几何方法的CSMA网络的现有模型存在两个重要的弱点：1）它们仅允许评估主要性能测量的空间平均值，从而隐藏了单个节点所实现的性能中可能存在的巨大差异; 2）它们仅在节点根据简单的空间过程（例如，Poisson点过程）。在本文中，我们将展示如何随机几何方法可以扩展到克服上述限制，允许获得节点的吞吐量分布以及分析一个重要的类的拓扑结构中，节点不独立放置。
Stochastic geometry proves to be a powerful tool for modeling dense wireless networks adopting random MAC protocols such as ALOHA and CSMA. The main strength of this methodology lies in its ability to account for the randomness in the nodes' location jointly with an accurate description at the physical layer, based on the SINR, that allows to consider also random fading on each link. Existing models of CSMA networks adopting the stochastic geometry approach suffer from two important weaknesses: 1) they permit to evaluate only spatial averages of the main performance measures, thus hiding possibly huge discrepancies in the performance achieved by individual nodes; 2) they are analytically tractable only when nodes are distributed over the area according to simple spatial processes (e.g., the Poisson point process). In this paper we show how the stochastic geometry approach can be extended to overcome the above limitations, allowing to obtain node throughput distributions as well as to analyze a significant class of topologies in which nodes are not independently placed.