Optimal GLRT-Based Robust Spectrum Sensing for MIMO Cognitive Radio Networks With CSI Uncertainty

In this paper, we develop generalized likelihood ratio test (GLRT)-based detectors for robust spectrum sensing in multiple-input multiple-output (MIMO) cognitive radio networks considering uncertainty in the available channel state information (CSI). Initially, for a scenario with known CSI uncertainty statistics, we derive the novel robust estimator-correlator detector (RECD) and the robust generalized likelihood detector (RGLD), which are robust against the uncertainty in the available estimates of the channel coefficients. Subsequently, for a scenario with unknown CSI uncertainty statistics, we develop a generalized likelihood ratio test (GLRT) based composite hypothesis robust detector (CHRD) for spectrum sensing. Closed form expressions are presented for the probability of detection (P
_D
) and the probability of false alarm (P
_FA
) to characterize the detection performance of the proposed robust spectrum sensing schemes. Further, a deflection coefficient based optimization framework is also developed and solved to derive closed form expressions for the optimal beacon sequences. Simulation results are presented to demonstrate the performance improvement achieved by the proposed robust spectrum sensing schemes and to verify the analytical results derived.