We study the sparse recovery problem with an underdetermined linear system characterized
by a Kronecker-structured dictionary and a Kronecker-supported sparse vector. We cast this …

This paper presents novel cascaded channel estimation techniques for an intelligent
reflecting surface-aided multiple-input multiple-output system. Motivated by the channel …

In this paper, we develop solutions for sparse tensor signal recovery (SR) and tensor
dictionary learning (DL) problems via the sparse Bayesian learning (SBL) approach. We …

We propose Weighted SBL (WSBL) for sparse signal recovery, inspired by the Sparse
Bayesian Learning (SBL) method. Unlike SBL, where all hyperparameter priors follow …

Dictionary learning (DL) is a well-researched problem, where the goal is to learn a dictionary
from a finite set of noisy training signals, such that the training data admits a sparse …

Sparse Beyesian learning is a popular approach for sparse signal recovery, and has
demonstrated superior performance in a series of experiments. Nevertheless, the sparse …

Sparse coding refers to the pursuit of the sparsest representation of a signal in a typically
overcomplete dictionary. From a Bayesian perspective, sparse coding provides a maximum …

Sparse Bayesian learning (SBL) has high computational complexity associated with matrix
inversion in each iteration. In this paper, we investigate complexity reduced multiple …

We address the sparse signal recovery problem in the context of multiple measurement
vectors (MMV) when elements in each nonzero row of the solution matrix are temporally …

We consider the problem of recovering block-sparse signals whose cluster patterns are
unknown a priori. Block-sparse signals with nonzero coefficients occurring in clusters arise …