Multiple Antenna Cyclostationary Spectrum Sensing Based on the Cyclic Correlation Significance Test

TL;DR

This paper introduces a multiple antenna spectrum sensing method using cyclostationarity and cyclic correlation significance testing, which improves detection robustness and reduces noise estimation dependence in cognitive radio networks.

Contribution

It presents a novel detection technique leveraging eigenvalues of cyclic covariance matrices and cyclic correlation significance tests, enhancing detection performance in fading and noisy environments.

Findings

Higher detection probability than existing methods

Robustness to noise uncertainty demonstrated

Threshold independence from noise covariance and sample size

Abstract

In this paper, we propose and analyze a spectrum sensing method based on cyclostationarity specifically targeted for receivers with multiple antennas. This detection method is used for determining the presence or absence of primary users in cognitive radio networks 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. Analytical expressions for the probability of detection and probability of false-alarm under both spatially uncorrelated or spatially correlated noise are derived and verified by simulation. The detection performance in a Rayleigh flat-fading environment is found and verified through simulations. One of the advantages of the proposed method is that the detection threshold is shown to be independent of both the number of samples and the noise covariance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown to provide higher detection probability and better robustness to noise uncertainty than existing multiple-antenna cyclostationary-based spectrum sensing algorithms under both AWGN as well as a quasi-static Rayleigh fading channel.