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Signal Reconstruction Performance under Quantized Noisy Compressed Sensing
Univ Oulu, Finland.
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
Univ Oulu, Finland.
2019 (English)In: 2019 DATA COMPRESSION CONFERENCE (DCC), IEEE , 2019, p. 586-586Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE , 2019. p. 586-586
Series
IEEE Data Compression Conference, ISSN 1068-0314
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:liu:diva-158886DOI: 10.1109/DCC.2019.00098ISI: 000470908200091ISBN: 978-1-7281-0657-1 (electronic)OAI: oai:DiVA.org:liu-158886DiVA, id: diva2:1337578
Conference
2019 Data Compression Conference (DCC)
Available from: 2019-07-16 Created: 2019-07-16 Last updated: 2019-10-31

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf