Spectrum sensing is a fundamental component is a cognitive radio. In this
paper, we propose new sensing methods based on the eigenvalues of the
covariance matrix of signals received at the secondary users. In particular,
two sensing algorithms are suggested, one is based on the ratio of the maximum
eigenvalue to minimum eigenvalue; the other is based on the ratio of the
average eigenvalue to minimum eigenvalue. Using some latest random matrix
theories (RMT), we quantify the distributions of these ratios and derive the
probabilities of false alarm and probabilities of detection for the proposed
algorithms. We also find the thresholds of the methods for a given probability
of false alarm. The proposed methods overcome the noise uncertainty problem,
and can even perform better than the ideal energy detection when the signals to
be detected are highly correlated. The methods can be used for various signal
detection applications without requiring the knowledge of signal, channel and
noise power. Simulations based on randomly generated signals, wireless
microphone signals and captured ATSC DTV signals are presented to verify the
effectiveness of the proposed methods.

本文提出了基于二级用户接收信号的协方差矩阵特征值的新型感知方法，包括基于最大特征值与最小特征值比率以及基于平均特征值与最小特征值比率的感知算法，并使用最新的随机矩阵理论量化了两种算法的误警概率和检测概率，验证了方法的有效性。