Optimizing Wideband Cyclostationary Spectrum Sensing Under Receiver Impairments

In the context of Cognitive Radios (CRs), cyclostationary detection of primary users (PUs) is regarded as a common method for spectrum sensing. Cyclostationary detectors rely on the knowledge of the signal's symbol rate, carrier frequency, and modulation class in order to detect the present cyclic features. Cyclic frequency and sampling clock offsets are the two receiver impairments considered in this work. Cyclic frequency offsets, which occur due to oscillator frequency offsets, Doppler shifts, or imperfect knowledge of the cyclic frequencies, result in computing the test statistic at an offset from the true cyclic frequency. In this paper, we analyze the effect of cyclic frequency offsets on conventional cyclostationary detection, and propose a new multi-frame test statistic that reduces the degradation due to cyclic frequency offsets. Due to the multi-frame processing of the proposed statistic, non-coherent integration might occur across frames. Through an optimization framework developed in this work that can be performed offline, we determine the best frame length that maximizes the average detection performance of the proposed cyclostationary detection method given the statistical distributions of the receiver impairments. As a result of the optimization, the proposed detectors is shown to achieve the performance gains over conventional detectors given the constrained sensing time. We derive the proposed detector's theoretical average detection performance, and compare it to the performance of the conventional cyclostationary detector. Our analysis shows that gains in average detection performance using the proposed method can be achieved when the effect of sampling clock offset is less severe than that of the cyclic frequency offset. The analysis given in this paper can be used as a design guideline for practical implementation of cyclostationary spectrum sensors.