Data-Assisted Low Complexity Compressive Spectrum Sensing on Real-Time Signals under Sub-Nyquist Rate
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In this paper, we consider a hybrid framework combining compressive spectrum sensing with geo-location database to find the spectrum holes in cognitive radio. In the hybrid frame- work, a geo-location database algorithm is proposed to be stored locally at secondary users (SUs) to remove the extra transmission link to a centralized remote geo-location database. Specifically, by utilizing the output of geo-location database algorithm, a data-assisted non-iteratively reweighted least squares (DNRLS) based compressive spectrum sensing algorithm is proposed to improve detection performance under sub-Nyquist sampling rates for wideband spectrum sensing, and to reduce the computational complexity during signal recovery. In addition, an efficient method for the calculation of maximum allowable equivalent isotropic radiated power in TV white space (TVWS) is also designed to further relax SUs. The convergence and complexity of the proposed DNRLS algorithm are analyzed theoretically. Furthermore, the proposed framework is tested on real-time signals and data after having been validated by simulated signals and data in TVWS.