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arXiv 1202.4385cs.ITcs.NImath.IT
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无线网络本地容量研究综述

An Overview of Local Capacity in Wireless Networks

Salman Malik, Philippe Jacquet

摘要:

本文介绍了一种度量的性能评估的媒体接入方案在无线自组织网络称为本地容量。虽然推导无线自组织网络的端到端容量是一个困难的问题,本地容量框架允许我们量化随机位于网络中的接收器节点接收的平均信息速率。在这篇文章中,基本的网络模型和分析工具进行了讨论,并应用到一个简单的网络,得出各种媒体接入方案的本地容量。我们的目标是确定最优的方案,并看看它如何与更实际的媒体接入方案相比。我们分析了网格模式计划,同时发射机定位在一个规则的网格模式,ALOHA计划,同时发射机根据均匀泊松分布和排除计划,同时发射机根据排除规则,如节点着色和载波侦听计划进行调度。我们的分析表明,本地容量是最佳的同时发射机定位在一个网格模式的基础上等边三角形和我们的研究结果表明,这种最佳的本地容量是最多的两倍的本地容量的ALOHA为基础的计划。我们的研究结果还表明,节点着色和载波侦听计划接近最佳的本地容量几乎可以忽略不计的差异。最后,我们还讨论了模型的不足以及未来的研究方向。

英文摘要:

This article introduces a metric for performance evaluation of medium access schemes in wireless ad hoc networks known as local capacity. Although deriving the end-to-end capacity of wireless ad hoc networks is a difficult problem, the local capacity framework allows us to quantify the average information rate received by a receiver node randomly located in the network. In this article, the basic network model and analytical tools are first discussed and applied to a simple network to derive the local capacity of various medium access schemes. Our goal is to identify the most optimal scheme and also to see how does it compare with more practical medium access schemes. We analyzed grid pattern schemes where simultaneous transmitters are positioned in a regular grid pattern, ALOHA schemes where simultaneous transmitters are dispatched according to a uniform Poisson distribution and exclusion schemes where simultaneous transmitters are dispatched according to an exclusion rule such as node coloring and carrier sense schemes. Our analysis shows that local capacity is optimal when simultaneous transmitters are positioned in a grid pattern based on equilateral triangles and our results show that this optimal local capacity is at most double the local capacity of ALOHA based scheme. Our results also show that node coloring and carrier sense schemes approach the optimal local capacity by an almost negligible difference. At the end, we also discuss the shortcomings in our model as well as future research directions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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