With the rapid proliferation of new technologies and services in the wireless domain,
spectrum scarcity has become a major concern. The allocation of the Industrial, Medical and …

Compressive sensing (CS) has received much attention in several fields such as digital
image processing, wireless channel estimation, radar imaging, and cognitive radio (CR) …

Cognitive radio (CR) has been considered as a potential candidate for addressing the
spectrum scarcity problem of future wireless networks. Since its conception, several …

This paper presents two new algorithms for wideband spectrum sensing at sub-Nyquist
sampling rates, for both single nodes and cooperative multiple nodes. In single-node …

Compressive sensing is a new signal processing paradigm that aims to encode sparse
signals by using far lower sampling rates than those in the traditional Nyquist approach. It …

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the
implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive …

Spectrum scarcity and under-utilization of the apportioned spectral resources by the
licensed users have led to the introduction of a new communication paradigm called …

We explore algorithms and limitations for sparse optimization problems such as sparse
linear regression and robust linear regression. The goal of the sparse linear regression …

Cognitive radio is a promising technology proposed to solve the scarcity of the radio
spectrum by opportunistically allocating the idle channels of the licensed users to …

Spectrum occupancy reconstruction is an important issue often encountered in collaborative
spectrum sensing in distributed cognitive radio networks (CRNs). This issue arises when the …