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Bayesian Compressive Sensing (BCS), introduced into wideband cognitive radio network (CRN), has been considered as a promising technique for its ability of accurately recovering a signal from far fewer samples than required by the Nyquist sampling theorem. However, as BCS algorithm modulates the number of measurements step by step through evaluating the error bars, it needs appreciable amounts of...
Cooperative spectrum sensing schemes can enable cognitive radio (CR) users to efficiently identify the unoccupied channels or spectrum holes, as well as overcome the impact of shadowing and fading. Considering the hardware limitation, compressive sensing (CS) is a solution scheme to alleviate the requirements on the receiver hardware, which can recover the wideband sparse signal sampled at sub-Nyquist...
Bayesian Compressive Sensing (BCS) can effectively relax the requirement of hardware operational bandwidth and perfectly recover sparse wideband signal at sub-Nyquist rate in wideband spectrum sensing. However, one of the problem of BCS is the long recovery time caused by the high computational complexity. In this paper, a PU Probability Prediction based Bayesian Compressive Sensing algorithm (PBCS)...
In the existing distributed cooperative spectrum sensing schemes, cooperation based on consensus algorithms is a research hotspot. However, the existing methods haven't embodied the reliability difference among cognitive radio users, so there is an enormous scope to improve the accuracy. In this paper, we propose a weighted average consensus-based distributed compressive spectrum sensing (PE-WDCS)...
By using Relevance Vector Machine (RVM) to solve the problem of sparse signal recovery, Bayesian Compressive Sensing (BCS) can obtain good performance in spectral discrete spike signal detection. However, in cognitive radio (CR) system, the spectrum of primary user's signal, which is continuous in narrowband and is block sparse in wideband, cannot be exactly recovered by BCS. In this paper, a Bayesian...
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