Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple Antennas

TL;DR

This paper introduces an eigenvalue-based spectrum sensing method for cognitive radio with multiple antennas, leveraging cyclic covariance matrices to detect primary users efficiently and accurately without needing noise estimation.

Contribution

It presents a novel eigenvalue-based detection technique that outperforms existing methods in fading channels and reduces computational complexity.

Findings

Achieves constant false alarm rate independent of noise variance

Outperforms existing algorithms in Rayleigh fading channels

Lower computational complexity than prior methods

Abstract

In this paper, we propose a signal-selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple-antenna capability. This method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. The analytical threshold for achieving constant false alarm rate using this detection method is presented, verified through simulations, and shown to be independent of both the number of samples used and the noise variance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown, through numerical simulations, to outperform existing multiple-antenna cyclostationary-based spectrum sensing algorithms under a quasi-static Rayleigh fading channel, in both spatially correlated and uncorrelated noise environments. The algorithm also has significantly lower computational complexity than these other approaches.