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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...
Compressed sensing-based wideband spectrum sensing approaches have gotten much attention owning to their advantage of relieving the pressure on high signal acquisition costs. Most of these approaches need to recover the signal or power spectrum, which require high computational complexity. This letter proposes a novel wideband sensing algorithm with no recovery (NoR) of spectral, where the location...
In order to reduce sampling costs and computational complexity in the signal reconstruction process of existing compressed spectrum sensing (CSS), we propose a novel two-step compressed spectrum sensing (TS-CSS) scheme exploiting correlation of occupation states between sensing periods. At the first step of TS-CSS, we detect busy sub-channels of last sensing period to find the sub-channels that are...
In wideband compressive spectrum sensing, when the number of occupied subbands in the monitored wideband increases, the existing compressive sensing approaches have to raise the sampling rate to maintain a desired sensing performance. What is worse, that will add computational complexity of the following signal reconstruction. To overcome this issue, this paper proposes a novel wideband spectrum sensing...
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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