• DocumentCode
    77551
  • Title

    High SNR Linear Estimation of Vector Sources

  • Author

    Behbahani, Alireza S. ; Eltawil, Ahmed M. ; Jafarkhani, Hamid

  • Author_Institution
    Center for Pervasive Commun. & Comput., Univ. of California, Irvine, Irvine, CA, USA
  • Volume
    3
  • Issue
    6
  • fYear
    2014
  • fDate
    Dec. 2014
  • Firstpage
    581
  • Lastpage
    584
  • Abstract
    In this letter, we extend our prior work and consider decentralized estimation of unknown random vectors under high observation signal-to-noise ratio (SNR). A linear model is considered for decentralized estimation of vector sources. Observation models and sensor operations are both linear. Furthermore, the channel between the wireless sensors and fusion center (FC) is a coherent multiple access channel (MAC). Each sensor observes a different vector source. Sensors are designed to minimize the total mean square error (MSE) at the FC subject to the individual transmit power constraints at the sensors. We first provide the solution for scalar sources under high observation SNR regime. Then, we use the provided solution for scalar sources and extend it to the case of vector sources.
  • Keywords
    mean square error methods; sensor fusion; wireless channels; wireless sensor networks; FC; coherent MAC; coherent multiple access channel; fusion center; power transmission; scalar source; signal-to-noise ratio; total MSE minimization; total mean square error minimization; unknown random vector source decentralized estimation; vector source high SNR linear estimation; wireless sensor network; Algorithm design and analysis; Covariance matrices; Estimation; Noise measurement; Signal to noise ratio; Wireless sensor networks; Distributed estimation; multiple access channel; wireless sensor networks;
  • fLanguage
    English
  • Journal_Title
    Wireless Communications Letters, IEEE
  • Publisher
    ieee
  • ISSN
    2162-2337
  • Type

    jour

  • DOI
    10.1109/LWC.2014.2359210
  • Filename
    6905740