Spectrum Sensing Algorithms for Cognitive MIMO Channels

The algorithms used in spectrum sensing have an important impact on the detection performance. Thus, in this paper, by exploiting the mathematical structure and physical properties of the signal's eigenvalues under correlated multiple-input multiple-output (MIMO) channels in cognitive radio (CR) networks, we propose two new spectrum sensing algorithms. Firstly, by applying the Torgerson theory of matrix majorization, we propose a new algorithm which is especially suitable for correlated CR-MIMO channels as in practical circumstances: maximum-geometric mean eigenvalues detector (MGM). Besides, we derive the expressions for both false alarm probability and threshold corresponding to MGM. Secondly, by applying the Gershgorin theorem on MGM, we propose a low-complexity detector based on MGM which reduces the computation complexity especially for high dimensional matrices. Simulation results show the effectiveness of the proposed algorithms, compared to other existing sensing methods even in low signal-to-noise ratios (SNR).