On the impact of the covariance matrix size for spectrum sensing methods:beamforming versus eigenvalues In this paper, we investigate the impact of the covariance matrix (CM) size of the received signal on the performance of spectrum sensing methods. Our analysis is based on two wellknown methods of spectrum sensing (SS), one based on the eigenvalues called maximumto minimum eigenvalue (MME) and the other is based on the beamforming technique called maximumtominimum beam energy (MMBE). From an analytical development, we provide an explanation about the gap of detection performance between the two algorithms according to the size of the CM. Finally, we provide some simulations results to show the impacts of the smoothing factor and the number of antennas on the detection performance for both methods. These two parameters define the size of the CM of the received signal.

A TensorBased Spectrum Sensing Technique for MIMO Cognitive Radio Networks Despite the numerous spectrum sensing techniques, the existing techniques fail in providing an efficient spectrum sensing whenever a hidden terminal problem arises. Meanwhile, this problem can happen at any time in any severely fading primarytosecondary channels resulting in very low primary signaltonoise ratios (SNRs) and hence ineffective detection of the primary user in a cognitive radio (CR). Towards overcoming this problem, by introducing a tensorbased hypothesis testing framework, this paper proposes an efficient tensorbased detector (TBD) for a multipleinput multipleoutput (MIMO) CR networks over multipath fading channels. MonteCarlo simulations demonstrate that TBD outperforms the generalized likelihood ratio test (GLRT) detector and maximumminimum eigenvalue (MME) detector, especially in the very low SNR regime which is a manifestation of the hidden terminal problem.