A review on polarization-based spectrum sensing

In cognitive radios, the biggest challenge is spectrum sensing. It usually makes use of the amplitude, frequency or space dimension of the received signal to differentiate signal from background noise. Polarization, also as an inherent characteristic of signals, provides an additional degree of freedom in signal space. Polarization-based spectrum sensing has been identified as an effective method for improving detection performance. In this paper, various polarization-based spectrum sensing methods including the optimal polarization likelihood ratio test using Neyman-Pearson theorem, virtual polarization detection utilizing maximum signal to noise ratio method and blind polarization detectors based on generalized likelihood ratio test method are reviewed. Then, a comprehensive comparison of various polarization-based sensing methods in terms of detection performance and computational complexity is detailed. Finally, challenges and possible future studies on polarization-based spectrum sensing are described. Copyright (c) 2015 John Wiley & Sons, Ltd.