Eigenvalue based double threshold spectrum sensing under noise uncertainty for cognitive radio

Abstract

Spectrum sensing is a fundamental problem in cognitive radio. The conventional energy based channel sensing method is highly vulnerable under low SNR and noise uncertainty conditions. The use of double threshold usually improves the detection performance, however, under noise uncertainty its performance deteriorates. Another method based on eigenvalues of sample covariance matrix has been studied in literature with single threshold. In this paper an eigenvalue based spectrum sensing technique with double threshold is proposed. The random matrix theory (RMT) is used to quantify the ratio of eigenvalues and derives the expression for the two thresholds required for reliable spectrum sensing. The proposed scheme overcomes the noise uncertainty and low SNR problems and out performs the conventional energy based detection method. The simulation results show that the proposed double threshold method based on eigenvalues exhibits better detection performance compared to conventional energy detection methods in terms of detection probability and number of samples required for reliable sensing.