Spectrum sensing of SC-FDMA signals in cognitive radio networks

As wireless technologies continue to advance the radio spectrum has become more congested. Spectrum utilization can be enhanced considerably by allowing a secondary user to use a licensed band when the primary user (PU) is not present. Cognitive radio (CR) promotes the efficient use of the spectrum. Cyclostationary detection is a method for detecting primary user transmissions by taking advantage of the cyclostationary features of the received signals. SC-FDMA is a multiple-access technique with low peak-to average-power ratio (PAPR) and reasonable complexity. A spectrum sensing technique for the discovery of the SC-FDMA signals is developed using a metric that makes use of the cyclostationary features of SC-FDMA signals. This metric is compared with a threshold to deduce the presence or absence of PU. The threshold is calculated using the Bayesian detection. The Bayesian decision rule is to minimise the expected posterior cost. The superiority of Bayesion detector over Neyman-Pearson detector, energy detector and cyclic prefix (CP) detector is compared. The performance of the detector is presented for additive white Gaussian noise and multipath Rayleigh fading channels. The influence of different FFT lengths and the number of users on the probability of detection is evaluated.