Numerical evidence suggests that compressive sensing (CS) approaches for wideband
massive MIMO channel estimation can achieve very good performance with limited training …

Exploiting the sparse user activity induced by sporadic transmission, compressed sensing
(CS) has been widely applied in multiple-input multiple-output (MIMO) enabled non …

We investigate the problem of recovering jointly r-rank and s-bisparse matrices from as few
linear measurements as possible, considering arbitrary measurements as well as rank-one …

We propose and analyze a solution to the problem of recovering a block sparse signal with
sparse blocks from linear measurements. Such problems naturally emerge inter alia in the …

The problem of wideband massive MIMO channel estimation is considered. Targeting for
low complexity algorithms as well as small training overhead, a compressive sensing (CS) …

Compressed sensing is a paradigm within signal processing that provides the means for
recovering structured signals from linear measurements in a highly efficient manner …

Extracting tomographic information about quantum states is a crucial task in the quest
towards devising high-precision quantum devices. Current schemes typically require …

Compressed sensing studies linear recovery problems under structure assumptions. We
introduce a new class of measurement operators, coined hierarchical measurement …

In this work, we study the restricted isometry property (RIP) of Kronecker-structured matrices,
formed by the Kronecker product of two factor matrices. Previously, only upper and lower …

A new family of operators, dubbed hierarchical measurement operators, is introduced and
discussed within the framework of hierarchically sparse recovery. A hierarchical …