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Constrained cluster based blind localization of primary user for cognitive radio networks

机译:基于受限用户的受限基于簇的认知无线网络的盲本地化

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Blind localization of primary user (PU) is a geo-location spectrum awareness feature that can be very useful in enhancing the functionality of cognitive radios (CRs) in terms of minimizing the interference to the PU. However, the estimation of the PU position within the region is made difficult because cooperation between the PU and the secondary user (SU) does not exist and therefore the PU signal parameters remain unknown to the SU. The centroid-based localization techniques have significantly been adopted as suitable candidates that do not require knowledge of such parameters. In this paper we investigate the localization performance of such techniques by imposing constraints to the selection of the SU nodes, termed as SU cluster, to estimate the PU location. In particular, we impose a minimum distance constraint between any two SU nodes and group the qualifying nodes into a cluster. Only the SU nodes from the constrained cluster can take part in localizing the PU. We simulate the proposed method for a shadow fading wireless environment and compare the results with the centroid and the weighted centroid based blind localization methods. Our results show that the mean squared error in the estimation of the position of the PU is significantly improved for the proposed method compared to the two standard centroid localization techniques especially when the true PU location is away from the center of the region.
机译:主用户(PU)的盲本地化是一种地理位置频谱意识特征,其在提高认知收音机(CRS)的功能方面非常有用,从而最小化对PU的干扰。然而,该区域内的PU位置的估计是困难的,因为PU与辅助用户(SU)之间的协作不存在,因此PU信号参数对SU保持不为人知。基于质心的定位技术已经显着被采用为不需要对这些参数知识的合适候选者。在本文中,我们通过对所谓的SU群集的苏节点施加约束来研究这种技术的本地化性能,以估计PU位置。特别是,我们在任何两个SU节点之间强制省略最小距离约束,并将限定节点分组到群集中。只有来自约束群集的SU节点可以参与本地化PU。我们模拟了阴影衰落无线环境的提出方法,并将结果与​​质心和加权基于质心的盲本地化方法进行比较。我们的结果表明,与两个标准质心定位技术相比,该方法估计PU位置估计中的平均平方误差是显着的,特别是当真实PU位置远离该区域的中心时,所提出的方法显着提高。

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