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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

Signal reconstruction performance under quantized noisy compressed sensing

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Author: Leinonen, Markus1; Codreanu, Marian2; Juntti, Markku1
Organizations: 1Centre for Wireless Communications, University of Oulu, Erkki Koiso-Kanttilan katu 3, Oulu, FI-90570, Finland
2Linköping University, Sweden
Format: abstract
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019060318153
Language: English
Published: IEEE Computer Society Press, 2019
Publish Date: 2019-06-03
Description:

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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Series: Proceedings. Data Compression Conference
ISSN: 1068-0314
ISSN-E: 2375-0391
ISSN-L: 1068-0314
ISBN: 978-1-7281-0657-1
ISBN Print: 978-1-7281-0658-8
Pages: 586 - 586
DOI: 10.1109/DCC.2019.00098
OADOI: https://oadoi.org/10.1109/DCC.2019.00098
Host publication: 2019 Data Compression Conference (DCC)
Conference: Data Compression Conference
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Compressed sensing
quantization
rate distortion
Abstract
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