A Collaborative Approach for Compressive Spectrum Sensing

Abstract

Compressive Sensing (CS) has been proven effective to elevate some of the problems associated with spectrum sensing in wideband Cognitive Radio (CR) networks through efficient sampling and exploiting the underlying sparse structure of the measured frequency spectrum. In this chapter, the authors discuss the motivation and challenges of utilizing collaborative approaches for compressive spectrum sensing. They survey the different approaches and the key published results in this domain. The authors present in detail an approach that utilizes Kronecker sparsifying bases to exploit the two-dimensional sparse structure in the measured spectrum at different, spatially separated cognitive radios. Simulation results show that the presented scheme can substantially reduce the Mean Square Error (MSE) of the recovered power spectrum density over conventional schemes while maintaining the use of a low-rate Sub-Nyquist Analog to Information Converter. It is also shows that one can achieve dramatically lower MSE under low compression ratios using a dense measurement matrix while using Nyquist rate ADC.