Spectrum-Sensing Algorithms for Cognitive Radio Based on Statistical Covariances
Spectrum-Sensing Algorithms for Cognitive Radio Based on Statistical Covariances
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DOI：
10.1109/tvt.2008.2005267
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发表时间：
2009-05-01
影响因子：
6.8
通讯作者：
Liang, Ying-Chang
中科院分区：
文献类型：
--
作者：
Zeng, Yonghong;Liang, Ying-Chang
频谱感知，即检测许可频谱中主要用户的存在，是认知无线电中的一个基本问题。由于接收信号和噪声的统计协方差通常不同，因此它们可以用于区分存在主用户信号的情况和仅存在噪声的情况。本文提出了基于从有限数量的接收信号样本计算出的样本协方差矩阵的频谱感知算法。然后从样本协方差矩阵中提取两个检验统计量。通过比较两个测试统计数据来决定是否存在信号。给出了所提出算法的理论分析。基于统计理论找到检测概率和相关阈值。这些方法不需要任何关于信号、信道和噪声功率的先验信息。此外，不需要同步。基于窄带信号、捕获的数字电视 (DTV) 信号和多个天线信号的仿真来验证这些方法。
Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem in cognitive radio. Since the statistical covariances of the received signal and noise are usually different, they can be used to differentiate the case where the primary user's signal is present from the case where there is only noise. In this paper, spectrum-sensing algorithms are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. Two test statistics are then extracted from the sample covariance matrix. A decision on the signal presence is made by comparing the two test statistics. Theoretical analysis for the proposed algorithms is given. Detection probability and the associated threshold are found based on the statistical theory. The methods do not need any information about the signal, channel, and noise power a priori. In addition, no synchronization is needed. Simulations based on narrow-band signals, captured digital television (DTV) signals, and multiple antenna signals are presented to verify the methods.