Wideband spectrum sensing based on coprime sampling

In wideband compressive spectrum sensing, when the number of occupied subbands in the monitored wideband increases, the existing compressive sensing approaches have to raise the sampling rate to maintain a desired sensing performance. What is worse, that will add computational complexity of the following signal reconstruction. To overcome this issue, this paper proposes a novel wideband spectrum sensing approach based on time-domain coprime sampling. Our approach first estimates the power spectrum of the wideband signal by employing the coprime sampling scheme, then performs detection via a subband-bin energy detector. The cornerstone of our approach is that we can generate a Nyquist space sampled autocorrelation from the sub-Nyquist samples by implementing the coprime sampling scheme. Demonstrated by the simulations, our proposed wideband sensing approach has a better performance under high compression ratio and a better robustness against noise compared with the orthogonal matching pursuit (OMP). Also it is illustrated that our approach has the advantage over computational complexity by analyzing. Considering the merits synthetically, the novel wideband sensing approach adapts better to a higher bandwidth by comparision.