Spectrum sensing, database, and its hybrid

Abstract

The rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate efficient usage of the underutilized spectrum. Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization, by allowing secondary users (SUs) to opportunistically access the temporarily unused spectrum, without introducing harmful interference to primary users (PUs). A crucial requirement in cognitive radio networks (CRNs) is wideband spectrum sensing, in which SUs should detect spectral opportunities across a wide frequency range. However, wideband spectrum sensing could lead to unaffordable high sampling rates at energy-constrained SUs. Sub-Nyquist sampling was developed to overcome this issue by exploiting the sparse property of the wideband signals. Additionally, to relax the sensing requirements, hybrid framework that combines the advantages of both geo-location database and spectrum sensing is explored. The experimental results show that the hybrid schemes can achieve improved detection performance with reduced hardware and computation complexity in comparison with the sensing and database only approach.