PROJECT TITLE :

Scheduled Sequential Compressed Spectrum Sensing for Wideband Cognitive Radios - 2018

ABSTRACT:

The support for high information rate applications with the cognitive radio technology necessitates wideband spectrum sensing. But, it's costly to apply long-term wideband sensing and is very difficult within the presence of uncertainty, like 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. Rather than invoking CS to reconstruct the signal in each amount, our proposed scheme performs backward grouped-compressed-information sequential likelihood ratio test (backward GCD-SPRT) using compressed data samples in sequential detection, while CS recovery is only pursued when required. This technique on one hand significantly reduces the CS recovery overhead, and on the opposite takes advantage of sequential detection to improve the sensing quality. Furthermore, we have a tendency to propose (a) an in-depth sensing scheme to accelerate sensing call-creating when a amendment in channel standing is suspected, (b) a block-sparse CS reconstruction algorithm to exploit the block sparsity properties of wide spectrum, and (c) a group of schemes to fuse results from the recovered spectrum signals to additional improve the general sensing accuracy. In depth performance analysis results show that our proposed schemes will considerably outperform peer schemes below sufficiently low SNR settings.