Formulated as a least square problem under an l 0 constraint, sparse signal restoration is a
discrete optimization problem, known to be NP complete. Classical algorithms include, by …

With the advent of the Internet of things, massive machine-type communications (mMTC)
have become one of the most important requirements for next generation communication …

This paper is devoted to the analysis of necessary (not sufficient) optimality conditions for the
ℓ _0 ℓ 0-regularized least-squares minimization problem. Such conditions are the roots of …

Taking advantage of the structures inherent in many sparse decompositions constitutes a
promising research axis. In this paper, we address this problem from a Bayesian point of …

Orthogonal greedy algorithms are popular sparse signal reconstruction algorithms. Their
principle is to select atoms one by one. A series of unconstrained least-square subproblems …

稀疏分解算法是稀疏表达理论和压缩感知理论中的核心问题, 也是当前信号处理领域的一个热门
话题. 近年来, 研究人员发现除了稀疏以外, 如果引入稀疏系数之间的相关性先验信息 …

A reliable support detection is essential for a greedy algorithm to reconstruct a sparse signal
accurately from compressed and noisy measurements. This paper proposes a novel support …

Sparse signal restoration is usually formulated as the minimization of a quadratic cost
function| y-Ax|| 2 2 where\mbi A is a dictionary and\mbi x is an unknown sparse vector. It is …

Channel estimation in high-mobility environments is a challenging problem for advanced
mobile communication systems (5G and beyond). In this manuscript, we first propose an …

Fitting penalized models for the purpose of merging the estimation and model selection
problem has become commonplace in statistical practice. Of the various regularization …