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CaSRA: An algorithm for cognitive tethering in dense wireless areas

机译:CaSRA:一种用于密集无线区域的认知网络共享算法

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This paper investigates the performance gain obtained by creating a hotspot-slave configuration of nodes in densely populated areas. In doing so, a semi-distributed algorithm, referred to as CaSRA (Clustering and Spectrum assignment and Resource Allocation), that allows the hotspots to tether over locally available white-spaces is proposed. CaSRA, performs in three-steps: 1) clusters the nodes based on the K-means clustering algorithm, 2) assigns white-space spectrum to each cluster based on a distributed graph-coloring approach to maximize spectrum reuse, and 3) allocates physical layer resources to individual users based on local channel information. Unlike small cells (femtocells, relays, and WiFi networks), this approach does not require any additions to the existing infrastructure, but allows the nodes, themselves, to act as hotspots. Simulation results show that given fixed amount of network resources, the proposed algorithm can significantly improve the overall performance of network users.
机译:本文研究了通过在人口稠密的区域中创建节点的热点-从属配置而获得的性能提升。在此过程中,提出了一种半分布式算法,称为CaSRA(群集和频谱分配和资源分配),该算法允许热点在本地可用的空白区域内进行束缚。 CaSRA分三个步骤执行:1)基于K-means聚类算法对节点进行聚类; 2)基于分布式图着色方法为每个聚类分配空白频谱,以最大程度地利用频谱; 3)分配物理资源根据本地渠道信息将资源分层分配给各个用户。与小型小区(毫微微小区,中继和WiFi网络)不同,此方法不需要对现有基础结构进行任何添加,但允许节点本身充当热点。仿真结果表明,在给定的网络资源量的情况下,该算法可以显着提高网络用户的整体性能。

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