Signal reconstruction performance under quantized noisy compressed sensing

Leinonen, Markus; Codreanu, Marian; Juntti, Markku (2019-05-13)
 
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URL:
https://doi.org/10.1109/DCC.2019.00098

Leinonen, Markus
Codreanu, Marian
Juntti, Markku
IEEE Computer Society Press
13.05.2019

M. Leinonen, M. Codreanu and M. Juntti, "Signal Reconstruction Performance Under Quantized Noisy Compressed Sensing," 2019 Data Compression Conference (DCC), Snowbird, UT, USA, 2019, pp. 586-586. doi: 10.1109/DCC.2019.00098

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doi:https://doi.org/10.1109/DCC.2019.00098
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https://urn.fi/URN:NBN:fi-fe2019060318153
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Abstract

We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS) schemes for acquiring sparse signals via quantized/encoded noisy linear measurements, motivated by low-power sensor applications. For such a quantized CS (QCS) context, the paper combines and refines our recent advances in algorithm designs and theoretical analysis. Practical symbol-by-symbol quantizer based QCS methods of different compression strategies are proposed. The compression limit of QCS — the remote RDF — is assessed through an analytical lower bound and a numerical approximation method. Simulation results compare the RD performances of different schemes.

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