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.

Matrix and TensorBased RFI Detectors for MultiAntenna Wireless Communications Radio frequency interference (RFI) is affecting various radio frequency operating systems. Mentioning of practical wireless channels, meanwhile, RFI can be received through a multipath fading channel. In such scenarios, robust detection of RFI can be challenging since the signal of interest can also be received through a multipath fading channel. To this end, by introducing a tensorbased hypothesis testing framework, this paper proposes a matrixbased RFI detector (MBRD) and a tensorbased RFI detector (TBRD) for RFI received through a multipath fading channel. Simulation results demonstrate that TBRD outperformsxe2x80x94especially for a weak RFIxe2x80x94MBRD and the generalized likelihood ratio test (GLRT) detector whenever the number of receiving antennas increases.