Cyclostationary property based spectrum sensing algorithms for primary detection in cognitive radio systems

Abstract

To implement the primary signal without interference in cognitive radio systems, cognitive radios can detect the presence of the primary user in low SNR. Currently, energy detector is the most common way of spectrum sensing because of its low computational complexity. However, performance of the method will be possibly degraded due to the uncertainty noise. This paper illustrates the benefits of one-order and two-order cyclostationary properties of primary user’s signals in time domain. These feature detection techniques in time domain possess the advantages of simple structure and low computational complexity comparing with spectral feature detection methods. Furthermore, performance of the one-order and two-order feature detection is studied and the analytical results are given. Our analysis and numerical results show that the sensing performance of the one-order feature detection is improved significantly comparing with conventional energy detector since it is robust to noise. Meanwhile, numerical results show that the two-order feature detection technique is better than the one-order feature detection. However, this benefit comes at the cost of hardware burdens and power consumption due to the additional multiplying algorithm.