Correlation-Statistics-Based Spectrum Sensing Exploiting Energy and Polarization for Dual-Polarized Cognitive Radios

In this paper, we consider the problem of spectrum sensing in cognitive radios by exploiting Stokes subvector, which can completely describe energy and polarization information of the received vector signal captured by dual-polarized antennas. We first find that both component correlation between Stokes variables (i.e., the elements of Stokes subvector) and vector correlation between Stokes subvectors containing signal and noise are different from that of noise only with high probability. Therefore, two new blind detectors, namely, component-correlation-based energy-polarization detection (CCB-EPD) and vector-correlation-based energy-polarization detection (VCB-EPD), are proposed, respectively. The analysis results reveal that CCB-EPD and VCB-EPD are all constant false alarm rate detectors, and the VCB-EPD method achieves better performance than CCB-EPD when channel is low depolarized and vice versa. Simulations show that the proposed two methods exhibit better performances than other multiantenna-based detectors whether priori polarization information of primary user is known or not. We also show that the two proposed methods have performance improvement with respect to existing polarization-based detectors due to the exploitation of both energy and polarization information and the unaffectedness by noise uncertainty. The experimental results verify that the proposed two methods can satisfy the performance requirement specified by the IEEE 802.22 standard.