Toward Overcoming a Hidden Terminal Problem Arising in MIMO Cognitive Radio Networks: A Tensor-Based Spectrum Sensing Algorithm

As the main enablers of cognitive radio (CR), numerous spectrum sensing techniques have been proposed to date. Despite the numerous techniques, the existing spectrum sensing techniques tend to fail in rendering an efficient spectrum sensing whenever a hidden terminal problem arises. Meanwhile, this problem can happen at any time in any severely fading primary-to-secondary channels resulting in very low primary signal-to-noise ratios (SNRs) and hence ineffective detection of the primary user. Toward overcoming this problem, by introducing a tensor-based hypothesis testing framework, this paper proposes an efficient tensor-based detector (TBD) for a multiple-input multiple-output (MIMO) CR networks over multi-path fading channels. For the proposed spectrum sensing technique, insightful asymptotic performance analyses are provided and Monte-Carlo simulations that assess its performance have been conducted. These simulations corroborate that TBD outperforms the generalized likelihood ratio test (GLRT) detector and the maximum-minimum eigenvalue (MME) detector, especially in the very low SNR regime which is a manifestation of the hidden terminal problem. Moreover, the simulations validate the derived asymptotic performance characterizations.