A Gaussian mixture model method for eigenvalue-based spectrum sensing with uncalibrated multiple antennas

In this article, a novel spectrum sensing technique for cognitive radio (CR) is proposed where eigen-values of the covariance matrix of the received signal are used as features for detection with multiple uncalibrated antennas. In the proposed scheme, training decision vectors composed of eigenvalue com-ponents are approximated as Gaussian mixture model (GMM) and underlying distribution parameters are extracted using expectation-maximization (EM) algorithm. Using the obtained parameters, posterior probability of subsequent decision vectors are computed and the channel is classified as either occu-pied or unoccupied. This is different form existing spectrum sensing techniques where elements of the covariance matrix or its eigenvalues are reduced to a single decision statistic resulting in loss of use-ful discriminatory information. Proposed technique overcomes this limitation by forming decision vectors from the eigenvalue features and performing GMM based classification in multidimensional space. Simu-lation results also reveal that proposed method outperforms state-of-the-art techniques for detection of primary user (PU) signal using uncalibrated antennas. (c) 2021 Elsevier B.V. All rights reserved.