  Wideband spectrum sensing (WSS) is an essential technology for cognitive
radio. However, the sampling rate is still a bottleneck of WSS. Several
sub-Nyquist sensing methods have been proposed. These technologies deteriorate
in the low signal to noise ratio (SNR) regime or suffer high computational
complexity. In this paper, we propose a novel sub-Nyquist WSS method based on
Multi-coset (MC) sampling. We design a simple SNR-robust and low-complexity
multiband signal detection algorithm. In particular, the proposed method
differs the commonly used detection algorithms which are based on energy
detection (ED), matched filter (MF) or cyclostationary detection (CD). We
exploit the linear recurrent relation between the locations of nonzero
frequencies and the DFT of the arithmetic-shifted subsampled signals. These
relations can be uniquely expressed by a series of the so-called frequency
locator polynomials (FLPs). The scalar of the relations is related to the
bandwidths of the subsignals. Basing on this, we propose a detector for sparse
multiband signals along with the method estimating carrier frequency and
bandwidth. The detector does not require priori knowledge about the frequency
locations of the signals of interest. Moreover, it has lower complexity of both
samples and computation compared to CD in sparse case. Experimental results
show the detector outperforms ED in the sub-Nyquist regime especially in low
SNRs.
