Compressed measurements based cyclostationary detector for wideband cognitive radios

Cyclostationary feature detection is one of the most powerful spectrum sensing techniquesused for cognitive radio (CR) systems. This is because of its robustness against noiseuncertainties. However, this technique needs high sampling rates, which is limited by thestate-of the-art analog to digital converters (ADCs), especially in wideband regime.Comressive sensing (CS) was used by many researchers for solving this problem viasub-Nyquist sampling rates. However CS solves the high sampling rate problem, but it doesnot reduce complexity considerably. This is because spectrum sensing is performed in threesteps: sensing compressed measurements, then reconstructing the Nyquist rate signal, andfinally performing cyclostationary detection (CD) on the reconstructed signal. In this paperwe suggest performing CD directly on the compressed measurements skipping thereconstruction step which is the most complex step in CS. This can be realized by designingthe sensing matrix with constraints different from those used in the conventional CS. Resultsshow that performance is improved relative to applying CD on the Nyquist rate signal. Thisis in addition to reduction in receiver complexity resulting from reducing sampling rates. Adetection probability of 78.7% can be achieved with only 7% of samples used by theconventional cyclostationary detection technique that achieves a detection probability of32.7%.