Spectrum sensing algorithms based on correlation statistics of polarization vector

• Polarization characteristic of signal is exploited for spectrum sensing.
• Statistical distributions are different between signal plus noise and noise only.
• Detectors achieve better performance with less depolarization effect of channel.
• Detectors have superior performance and moderate computational complexity.

In this paper, we consider the problem of spectrum sensing in cognitive radios (CRs) by exploiting inherent polarization characteristics of signal. Since polarization vector can completely describe the radiation׳s polarization characteristics, we first derive the probability density function (PDF) of received polarization vector and its moments. Then we find the fact that both component and serial correlations of received polarization vector (i.e., the mixed polarization vector of primary signal and noise) are different from those of noise with high probability. This distinctive difference can be used to decide whether the primary signal exists or not. Therefore, component correlation sensing (CCS) algorithm and serial correlation sensing (SCS) algorithm are proposed respectively. Furthermore the closed-form expressions of probabilities of false alarm and detection are available for CCS and SCS algorithms. Simulations show that both CCS and SCS detectors achieve better performance with higher cross-polar discrimination (XPD) and polarization channel correlation coefficients. We also show that, if channel is highly depolarized, CCS performs better than SCS. Otherwise, the latter shows better performance. Compared with existing polarization based detectors, both CCS and SCS detectors perform better in the presence of noise power uncertainty.