Hierarchical compressed sensing

TL;DR

This paper extends compressed sensing theory to hierarchically structured signals, proposing efficient recovery algorithms with guarantees and demonstrating applications in communications and quantum tomography.

Contribution

It introduces a hierarchical compressed sensing framework with novel algorithms and theoretical guarantees for structured signals beyond sparsity.

Findings

Algorithms based on hierarchical hard-thresholding are guaranteed to converge.

Recovery is stable against noise and model mismatches.

Conditions for measurement ensembles to ensure accurate recovery are established.

Abstract

Compressed sensing is a paradigm within signal processing that provides the means for recovering structured signals from linear measurements in a highly efficient manner. Originally devised for the recovery of sparse signals, it has become clear that a similar methodology would also carry over to a wealth of other classes of structured signals. In this work, we provide an overview over the theory of compressed sensing for a particularly rich family of such signals, namely those of hierarchically structured signals. Examples of such signals are constituted by blocked vectors, with only few non-vanishing sparse blocks. We present recovery algorithms based on efficient hierarchical hard-thresholding. The algorithms are guaranteed to converge, in a stable fashion both with respect to measurement noise as well as to model mismatches, to the correct solution provided the measurement map acts isometrically restricted to the signal class. We then provide a series of results establishing the required condition for large classes of measurement ensembles. Building upon this machinery, we sketch practical applications of this framework in machine-type communications and quantum tomography.