5 citations · 5 across the 6 of their papers we have counts for
6 papers
Total Variation Sparse Bayesian Learning for Block Sparsity via Majorization-Minimization
Yanbin He, Geethu Joseph
Block sparsity is a widely exploited structure in sparse recovery, offering significant gains when signal blocks are known. Yet, practical signals often exhibit unknown block bound…
Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured Signals
Yanbin He, Geethu Joseph
This work studies the problem of jointly estimating unknown parameters from Kronecker-structured multidimensional signals, which arises in applications like intelligent reflecting…
A Hierarchical View of Structured Sparsity in Kronecker Compressive Sensing
Yanbin He, Geethu Joseph
Kronecker compressed sensing refers to using Kronecker product matrices as sparsifying bases and measurement matrices in compressed sensing. This work focuses on the Kronecker comp…
Kronecker-structured Sparse Vector Recovery with Application to IRS-MIMO Channel Estimation
Yanbin He, Geethu Joseph
This paper studies the problem of Kronecker-structured sparse vector recovery from an underdetermined linear system with a Kronecker-structured dictionary. Such a problem arises in…
Bayesian Algorithms for Kronecker-structured Sparse Vector Recovery With Application to IRS-MIMO Channel Estimation
Yanbin He, Geethu Joseph
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…
Structure-aware Sparse Bayesian Learning-based Channel Estimation for Intelligent Reflecting Surface-aided MIMO
Yanbin He, Geethu Joseph
This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular…