A compressive sampling approach for spectrum sensing and terminals localization in cognitive radio networks

In this paper, we propose to analyze and combine two of the main enabling features of cognitive radio: location awareness and spectrum sensing with taking into account one of the most challenging hardware limitation that cognitive radio may suffer from: signal acquisition at a Nyquist rate. During the problem formulation and when analyzing more deeply the equations related to each question apart, we will make the link between the formulation of spectrum sensing, location awareness and the hardware limitation by describing those problems in a unique compressed sensing formalism. Via the proposed framework, and compared to what has already been proposed, we made it possible to overcame another challenging postulate of fixed frequency spectrum allocation by also estimating the spectrum usage boundaries dynamically and in a fully blind way.