Compressive spectrum sensing front-ends for cognitive radios

We propose a novel parallel mixed-signal compressive spectrum sensing architecture for cognitive radios (CRs) with a detailed study of the signal modeling. The mixed-signal compressive sensing is realized with a parallel segmented compressive sensing (PSCS) architecture, which not only can filter out all the harmonic spurs that leak from the local random generator, but also provides a tradeoff between the sampling rate and the system complexity such that a practical hardware implementation is possible. We consider application of the architecture to do spectrum estimation, which is the first step for spectrum sensing in CRs. The benefit of prior knowledge about the input signal´s structure is explored and it is shown that this can be exploited in the PSCS architecture to greatly reduce the sampling rate.