Sparse Spectrum Sensing with Sub-Block Partition for Cognitive Radio Systems

Cognitive users are expected to be capable of exploring spectrum holes over a wide range of frequencies. Motivated by the sparse characteristic of underutilized spectrum, we consider sparse spectrum sensing using compressive sensing techniques for cognitive orthogonal frequency division multiplexing (OFDM) systems. The spectrum sensing problem is formulated as a multi-subcarrier detection problem, solved via the composite hypothesis testing and Neyman-Pearson criterion. Considering the availability of channel state information (CSI) at the cognitive device, two sparse spectrum sensing approaches are proposed for detecting the compressive received signals in time domain. For the purpose of complexity reduction, we further incorporate a sub-block partition scheme into the proposed approaches to leverage the spareness of the spectrum occupancy. The proposed approaches enable a flexible tradeoff between the implementation complexity and the sensing accuracy for wideband cognitive radios.