Data-assisted sub-Nyquist spectrum sensing

Abstract

Spectrum sensing is the first step to realize dynamic spectrum sharing. Geo-location database can enable white space devices (WSDs) to make use of the vacant spectrum without harmfully interfering with incumbent services. Dynamic changes of the wireless environment pose significant challenges to the database approach. Wideband spectrum sensing can detect the instant spectral opportunities over the wide frequency range, but the high sampling rate is hard to implement in the power-limited devices. To relax the sensing requirements on the WSDs, hybrid framework that combines the advantages of both geo-location database and spectrum sensing is explored in this paper. To further reduce the sampling bottleneck at high frequency, sub-Nyquist sampling techniques are adopted by exploiting the sparse property of the wideband signals. The experimental results show that the proposed hybrid schemes can achieve improved detection performance with reduced hardware and computation complexity in comparison with the sensing and database only approach.