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Cooperative blind localization of primary user in a cognitive radio environment

机译:认知无线电环境中主要用户的合作盲目定位

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Blind localization has emerged as an important topic addressing the challenges faced by any cognitive radio (CR) network. The estimation of the primary user (PU) position in a CR network is made difficult because generally cooperation between the PU and the secondary user (SU) does not exist. This presents a challenge since PU parameters such as transmit power, path loss exponent, noise variance and the location of the PU are not known. Hence, the need for a robust, accurate and range-free localization technique that does not require any knowledge of such parameters. In this paper, we investigate the localization performance when combining the database technique and the centroid localization techniques. We adopt a two-stage localization strategy. In the first stage, a mean-square error (MSE) method is used to find a close match between the actual received power and the database power and subsequently the PU estimates. Centroid localization (CL) and weighted centroid localization (WCL) techniques are then adopted in the second-stage as fusion strategies to give an estimate of the PU. We also analyse the root-mean squared error (RMSE) of the CL and WCL techniques. We further compare our results with the direct centroid localization techniques without involving the database.
机译:盲目定位已经成为解决任何认知无线电(CR)网络所面临挑战的重要主题。由于通常不存在PU和辅助用户(SU)之间的协作,因此难以估计CR网络中的主要用户(PU)位置。由于PU参数(例如发射功率,路径损耗指数,噪声方差和PU的位置)未知,因此提出了挑战。因此,需要一种鲁棒的,准确的和无范围的定位技术,该技术不需要任何此类参数的知识。在本文中,我们研究了结合数据库技术和质心定位技术时的定位性能。我们采用两阶段的本地化策略。在第一阶段,使用均方误差(MSE)方法查找实际接收功率与数据库功率之间的紧密匹配,然后查找PU估计值。然后在第二阶段采用质心定位(CL)和加权质心定位(WCL)技术作为融合策略,以给出PU的估计值。我们还分析了CL和WCL技术的均方根误差(RMSE)。我们进一步将结果与不涉及数据库的直接质心定位技术进行比较。

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