• DocumentCode
    2286758
  • Title

    Energy-efficient collaborative scheme for compressed sensing-based spectrum detection in cognitive radio networks

  • Author

    An, Chunyan ; Ji, Hong ; Li, Yi

  • Author_Institution
    Key Lab. of Universal Wireless Commun., Beijing Univ. of Posts & Telecommun., Beijing, China
  • fYear
    2012
  • fDate
    1-4 April 2012
  • Firstpage
    1360
  • Lastpage
    1364
  • Abstract
    Due to the potential detection error caused by information loss in sampling process, collaborative scheme is especially important for compressed sensing-based spectrum detection to improve the detection accuracy. In this paper, a novel energy-efficient low-complexity collaborative scheme is proposed for cognitive radio networks. In the proposed scheme, based on the prediction results of signal sparsity level by Lempel-Ziv-based prediction algorithm, the number of detection devices for spectrum detection is evaluated with the aim of minimizing the objective function, which takes into account both the detection accuracy and energy consumption. Finally, extensive simulation results are presented to show the effectiveness of our proposed collaborative scheme by comparing with the existing ones.
  • Keywords
    cognitive radio; energy conservation; energy consumption; Lempel-Ziv-based prediction algorithm; cognitive radio networks; collaborative scheme; compressed sensing-based spectrum detection; detection accuracy; detection devices; energy consumption; energy-efficient collaborative scheme; energy-efficient low-complexity collaborative scheme; potential detection error; sampling process; spectrum detection; Accuracy; Cognitive radio; Collaboration; Energy consumption; Markov processes; Prediction algorithms; Sensors; Signal sparsity level prediction; cognitive radio networks; compressed sensing; spectrum detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Wireless Communications and Networking Conference (WCNC), 2012 IEEE
  • Conference_Location
    Shanghai
  • ISSN
    1525-3511
  • Print_ISBN
    978-1-4673-0436-8
  • Type

    conf

  • DOI
    10.1109/WCNC.2012.6213991
  • Filename
    6213991