Block-length dependent thresholds in block-sparse compressed sensing
Mihailo Stojnic

TL;DR
This paper investigates how the success thresholds of block-sparse compressed sensing algorithms depend on block-length, providing precise bounds on allowable sparsity levels as functions of block-length.
Contribution
It introduces a model where block-length is a parameter, deriving sharp lower bounds on block-sparsity thresholds based on block-length in compressed sensing.
Findings
Derived sharp lower bounds on block-sparsity thresholds
Established thresholds as functions of block-length
Extended previous results to variable block-lengths
Abstract
One of the most basic problems in compressed sensing is solving an under-determined system of linear equations. Although this problem seems rather hard certain -optimization algorithm appears to be very successful in solving it. The recent work of \cite{CRT,DonohoPol} rigorously proved (in a large dimensional and statistical context) that if the number of equations (measurements in the compressed sensing terminology) in the system is proportional to the length of the unknown vector then there is a sparsity (number of non-zero elements of the unknown vector) also proportional to the length of the unknown vector such that -optimization algorithm succeeds in solving the system. In more recent papers \cite{StojnicICASSP09block,StojnicJSTSP09} we considered the setup of the so-called \textbf{block}-sparse unknown vectors. In a large dimensional and statistical context, we…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Photoacoustic and Ultrasonic Imaging · Random lasers and scattering media
