Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs)
in wireless communications systems requires estimation of the channels to and from the RIS …

Terahertz (THz) communications recently attract significant attention and become an
emerging technology pillar for sixth generation (6G) wireless systems. Due to the serious …

Compressive sensing generally relies on the ℓ 2 norm for data fidelity, whereas in many
applications, robust estimators are needed. Among the scenarios in which robust …

This paper addresses the issue of robust sparse recovery in compressive sensing (CS) in
the presence of impulsive measurement noise. Recently, robust data-fitting models, such as …

This paper addresses the robust reconstruction problem of a sparse signal from compressed
measurements. We propose a robust formulation for sparse reconstruction that employs the …

This letter investigates the problem of sparse signal recovery in the presence of additive
impulsive noise. The heavytailed impulsive noise is well modeled with stable distributions …

Coded aperture compressive spectral imagers (CSI) sense a three-dimensional data cube
by using two-dimensional projections of the coded and spectrally dispersed input image …

Sparse representation has achieved great success across various fields including signal
processing, machine learning and computer vision. However, most existing sparse …

Traditional compressive sensing (CS) primarily assumes light-tailed models for the
underlying signal and/or noise statistics. Nevertheless, this assumption is not met in the case …

Impulsive noise is always present in real-world image Compressive Sensing (CS)
acquisition systems, where existing CS reconstruction performance may seriously …