On the SNR Variability in Noisy Compressed Sensing
Lavrenko A, Romer F, Galdo GD, Thoma R (2017)
Publication Type: Journal article
Publication year: 2017
Journal
Book Volume: 24
Pages Range: 1148-1152
Article Number: 7888934
Journal Issue: 8
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.
Involved external institutions
Germany (DE)How to cite
APA:
Lavrenko, A., Romer, F., Galdo, G.D., & Thoma, R. (2017). On the SNR Variability in Noisy Compressed Sensing. IEEE Signal Processing Letters, 24(8), 1148-1152. https://doi.org/10.1109/LSP.2017.2689243
MLA:
Lavrenko, Anastasia, et al. "On the SNR Variability in Noisy Compressed Sensing." IEEE Signal Processing Letters 24.8 (2017): 1148-1152.
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