Time-Efficient Wideband Spectrum Sensing Based on Compressive Sampling

Compressed spectrum sensing (CSS) is proposed to detect spectrum opportunities efficiently over a wideband. However, most of existing CSS approaches will cause high computation costs for signal recovery when spectrum bandwidth goes large. As a result, it prolongs time for spectrum detection, which however runs counter to the original purpose of finding out spectrum opportunities over a wideband as rapidly as possible. To reduce the time consumed in signal reconstruction and realize real- time detection, we propose a novel decomposition compressed spectrum sensing (D-CSS) scheme. In D- CSS, a sparse sampling matrix is constructed first, and then it equivalently means a decomposition of the reconstructing process into two recovery subtasks. In doing so, we can scale down the overall problem and reduce the entire time for wideband spectrum detection compared with current CSS methods for a given desired sensing accuracy. Furthermore, the sparse character of our designed sampling matrix not only facilitates the operations of signal sampling and signal recovery, but also relieves the burden on random seeds generator and memory storage, which alleviates the overall implementation cost in CR practice.