Spectrum sensing is a fundamental component is a cognitive radio. In this project, a new sensing method is designed using MATLAB 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 the minimum eigenvalue. In practice, we only have a finite number of samples. Hence, we can only obtain the sample covariance matrix other than the statistic covariance matrix. Based on the sample covariance matrix, we propose two detection methods as follows,

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 are presented to verify the effectiveness of the proposed methods.

Reference Paper: Eigenvalue based Spectrum Sensing Algorithms for Cognitive Radio

Author’s Name: Yonghong Zeng and Ying-Chang Liang

Source: IEEE


Request source code for academic purpose, fill REQUEST FORM or contact +91 7904568456 by WhatsApp or sales@verilogcourseteam.com, fee applicable.

SIMULATION VIDEO DEMO