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A maximum-minimum eigenvalue detection simpler method based on secondary users locations for cooperative spectrum sensing

机译:基于次要用户位置的最大最小特征值检测简化方法

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Eigenvalue-based cooperative spectrum sensing, because of its robustness, has attracted a lot of attention. Computational complexity is a major drawback of this method. In this paper, to improve the detection probability and to reduce the number of affective secondary users, we investigate the effect of secondary users different distances from primary user base station with a partial clustering of secondary users. Monte-Carlo simulation results show the effectiveness of proposed method specially in low SNR values compared to other methods.
机译:基于特征值的协作频谱感知技术由于其鲁棒性而备受关注。计算复杂度是该方法的主要缺点。在本文中,为了提高检测概率并减少情感上的次要用户数量,我们研究了次要用户与主要用户基站之间的不同距离以及次要用户的部分聚类对次要用户的影响。蒙特卡洛仿真结果表明,与其他方法相比,该方法在低SNR值下特别有效。

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