Scheduled Sequential Compressed Spectrum Sensing for Wideband Cognitive Radios

The support for high data rate applications with the cognitive radio technology necessitates wideband spectrum sensing. However, it is costly to apply long-term wideband sensing and is especially difficult in the presence of uncertainty, such as high noise, interference, outliers, and channel fading. In this work, we propose scheduling of sequential compressed spectrum sensing which jointly exploits compressed sensing (CS) and sequential periodic detection techniques to achieve more accurate and timely wideband sensing. Instead of invoking CS to reconstruct the signal in each period, our proposed scheme performs backward grouped-compressed-data sequential probability ratio test (backward GCD-SPRT) using compressed data samples in sequential detection, while CS recovery is only pursued when needed. This method on one hand significantly reduces the CS recovery overhead, and on the other takes advantage of sequential detection to improve the sensing quality. Furthermore, we propose (a) an in-depth sensing scheme to accelerate sensing decision-making when a change in channel status is suspected, (b) a block-sparse CS reconstruction algorithm to exploit the block sparsity properties of wide spectrum, and (c) a set of schemes to fuse results from the recovered spectrum signals to further improve the overall sensing accuracy. Extensive performance evaluation results show that our proposed schemes can significantly outperform peer schemes under sufficiently low SNR settings.