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  4. On the SNR variability in noisy compressed sensing
 
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2017
Journal Article
Title

On the SNR variability in noisy compressed sensing

Abstract
Compressed sensing (CS) is a sampling paradigm that allows us to simultaneously measure and compress signals that are sparse or compressible in some domain. The choice of a sensing matrix that carries out the measurement has a defining impact on the system performance and it is often advocated to draw its elements randomly. It has been noted that in the presence of input (signal) noise, the application of the sensing matrix causes signal-to-noise ratio (SNR) degradation due to the noise folding effect. In fact, it might also result in the variations of the output SNR in compressive measurements over the support of the input signal, potentially resulting in unexpected nonuniform system performance. In this letter, we study the impact of a distribution from which the elements of a sensing matrix are drawn on the spread of the output SNR. We derive analytic expressions for several common types of sensing matrices and show that the SNR spread grows with the decrease of the number of measurements. This makes its negative effect especially pronounced for high compression rates that are often of interest in CS.
Author(s)
Lavrenko, Anastasia
Technische Universität Ilmenau
Römer, Florian
TU Ilmenau
Galdo, Giovanni del  
Thomä, Reiner
Journal
IEEE Signal Processing Letters  
Open Access
DOI
10.1109/LSP.2017.2689243
Additional full text version
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Language
English
Fraunhofer-Institut für Integrierte Schaltungen IIS  
Keyword(s)
  • compressed sensing

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