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Performance Limits of Greedy Maximal Matching in Multi-hop Wireless Networks

机译:多跳无线网络中贪婪最大匹配的性能限制

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In this paper, we characterize the performance limits of an important class of scheduling schemes, called Greedy Maximal Matching (GMM), for multi-hop wireless networks. For simplicity, we focus on the well-established node-exclusive interference model, although many of the stated results can be readily extended to more general interference models. The study of the performance of GMM is intriguing because although a lower bound on its performance is well known, empirical observations suggest that this bound is quite loose, and that the performance of GMM is often close to optimal. In fact, recent results have shown that GMM achieves optimal performance under certain conditions. In this paper, we provide new analytic results that characterize the performance of GMM through the topological properties of the underlying graphs. To that end, we generalize a recently developed topological notion called the local pooling condition to a far weaker condition called the σ-local pooling. We then define the local-pooling factor on a graph, as the supremum of all σ such that the graph satisfies σ-local pooling. We show that for a given graph, the efficiency ratio of GMM (i.e., the worst-case ratio of the throughput of GMM to that of the optimal) is equal to its local-pooling factor. Further, we provide results on how to estimate the local-pooling factor for arbitrary graphs and show that the efficiency ratio of GMM is no smaller than d{sup}*/(2d{sup}* - 1) in a network topology of maximum node-degree d{sup}*. We also identify a specific network topology for which the efficiency ratio of GMM is strictly less than 1.
机译:在本文中,我们对多跳无线网络表示了称为贪婪最大匹配(GMM)的重要调度方案的性能限制。为简单起见,我们专注于良好的节点专用干扰模型,尽管许多陈述的结果可以容易地扩展到更一般的干扰模型。对GMM性能的研究是有趣的,因为虽然其性能下较低的界限是众所周知的,但经验观察表明,这种界限非常松散,并且GMM的性能通常接近最佳。事实上,最近的结果表明,GMM在某些条件下实现了最佳性能。在本文中,我们提供了新的分析结果,其通过底层图的拓扑特性来表征GMM的性能。为此,我们概括了最近开发的拓扑概念,称为本地汇集条件的拓扑概念到较远的较弱条件,称为Σ-incoupling。然后,我们在图中定义了局部汇集因子,作为所有σ的超级Σ,使得图表满足Σ-inal池。我们表明,对于给定的图中,GMM的效率比(即,吞吐量GMM的到优选的最坏情况下的比值)等于其本地合并因子。此外,我们提供了如何估算任意图表的局部汇集因子的结果,并显示GMM的效率比在最大的网络拓扑中不小于D {SUP} * /(2d {sup} * -1)节点度d {sup} *。我们还确定了特定的网络拓扑结构,其中GMM的效率比严格小于1。

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