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Leveraging the Algebraic Connectivity of a Cognitive Network for Routing Design

机译:利用认知网络的代数连接性进行路由设计

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In this paper, we consider the implications of spectrum heterogeneity on connectivity and routing in a Cognitive Radio Ad Hoc Network (CRAHN). We study the Laplacian spectrum of the CRAHN graph when the activity of primary users is considered. We introduce the cognitive algebraic connectivity, i.e., the second smallest eigenvalue of the Laplacian of a graph, in a cognitive scenario. Throughout this notion we provide a methodology to evaluate the connectivity of CRAHNs and consequently introduce a utility function that is shown to be effective in capturing key characteristics of CRAHN paths. This model provides a unique metric that captures network connectivity, path length, and impact of primary users. Moreover, the proposed metric penalizes paths where spectrum band switchings are highly probable. We design all the components of our routing framework, named Gymkhana, and we present a twofold performance verification: one from a topological perspective to show all the potentialities of the proposed routing approach, and the other considering network traffic to evaluate the performance in terms of end-to-end delay and packet delivery ratio.
机译:在本文中,我们考虑了频谱异质性对认知无线电自组织网络(CRAHN)中的连接性和路由的影响。考虑主要用户的活动时,我们研究了CRAHN图的拉普拉斯谱。在认知场景中,我们介绍了认知代数连通性,即图的拉普拉斯算子的第二最小特征值。在整个概念中,我们提供了一种评估CRAHN连通性的方法,因此引入了一种效用函数,该函数在捕获CRAHN路径的关键特征方面很有效。该模型提供了一个独特的度量标准,可以捕获网络连接性,路径长度和主要用户的影响。此外,所提出的度量标准惩罚了频谱切换非常可能的路径。我们设计了名为“ Gymkhana”的路由框架的所有组件,并进行了双重性能验证:一个是从拓扑的角度显示所提出的路由方法的所有潜力,另一个是考虑网络流量以评估性能。端到端延迟和数据包传递比率。

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