Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

The extra degrees of freedom resulting from the consideration of Reconfigurable Intelligent
Surfaces (RISs) for smart signal propagation can be exploited for high accuracy localization …

Support recovery of sparse signals from noisy measurements with orthogonal matching
pursuit (OMP) has been extensively studied. In this paper, we show that for any K-sparse …

There is much discussion in industry and academia about possible technical solutions to
address the growth in demand for wireless broadband. Massive multiple-input multiple …

A sufficient condition reported very recently for perfect recovery of a K-sparse vector via
orthogonal matching pursuit (OMP) in K iterations (when there is no noise) is that the …

Sparse recovery aims to reconstruct sparse signals from compressed linear measurements.
In this paper, we propose a sparse recovery algorithm called multiple orthogonal least …

Spectrum sensing is a challenging issue in cognitive radio network. In particular, wideband
spectrum sensing gains more attention due to emerging 5G wireless networks characterized …

Support recovery of sparse signals from compressed linear measurements is a fundamental
problem in compressed sensing (CS). In this article, we study the orthogonal matching …

In millimeter wave (mmWave) communication systems, large antenna arrays are used to
compensate high path loss. While the large array provides high beamforming gain, it also …

In this paper, the joint support recovery of several sparse signals whose supports exhibit
similarities is examined. Each sparse signal is acquired using the same noisy linear …