• DocumentCode
    659120
  • Title

    Optimal binary measurement matrices for compressed sensing

  • Author

    Tehrani, Arash Saber ; Dimakis, Alexandros G. ; Caire, Giuseppe

  • Author_Institution
    Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
  • fYear
    2013
  • fDate
    9-13 Sept. 2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    We explicitly construct binary measurement matrices with good sparse approximation guarantees. Specifically, our measurement matrices have an order optimal number of measurements and have ℓ1/ℓ1 approximation guarantee. Our construction uses the progressive edge growth technique. We apply coding theoretic results and rely on a recent connection of compressed sensing to LP relaxation for channel decoding.
  • Keywords
    channel coding; compressed sensing; linear predictive coding; matrix algebra; LP relaxation; channel decoding; coding theoretic results; compressed sensing; optimal binary measurement matrices; order optimal number; progressive edge growth technique; sparse approximation guarantees; Approximation methods; Compressed sensing; Decoding; Parity check codes; Sparse matrices; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory Workshop (ITW), 2013 IEEE
  • Conference_Location
    Sevilla
  • Print_ISBN
    978-1-4799-1321-3
  • Type

    conf

  • DOI
    10.1109/ITW.2013.6691243
  • Filename
    6691243
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=659120