Spectrum sensing is a fundamental component in 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. Index Terms-Signal detection, spectrum sensing, sensing algorithm, cognitive radio, random matrix, eigenvalues, IEEE 802.22 wireless regional area networks (WRAN).
The Maximum to Minimum Eigenvalue (MME) and Energy with Minimum Eigenvalue (EME) algorithms for SS have been proposed in REF and approximate expressions for probability of a false alarm (P f ) and probability of detection (P d ) have been derived using RMT.
Eigenvalue-based spectrum sensing algorithms for cognitive radio
IEEE Transactions on Communications
IEEE Transactions on Communications
Computer Science Mathematics