Spectrum sensing in uncalibrated MIMO-based cognitive radios

Abstract

This paper addresses the problem of spectrum sensing (SS) in cognitive radio networks utilizing multiple-input multiple-output (MIMO) technology, even when the receivers are not calibrated. The study develops a framework by formulating various composite hypothesis testing SS problems based on different assumptions about the parameter space of the alternative hypothesis. To accomplish this, four new detectors are designed using the likelihood ratio test (LRT), Rao, and matrix information geometry (MIG) principles. We demonstrate analytically that three of the proposed detectors maintain a constant false alarm rate (CFAR) despite noise covariance matrix uncertainty. For the non-CFAR detectors, we introduce a novel analytical threshold-setting method to ensure that their false alarm probabilities remain below a predetermined level, thereby making them useful for practical situations. Finally, extensive simulations are conducted to verify the analytical results, assess false alarm regulation, and compare the proposed SS algorithms with existing approaches in terms of detection probability.