• DocumentCode
    1024748
  • Title

    Improved Particle Filtering-Based Estimation of the Number of Competing Stations in IEEE 802.11 Networks

  • Author

    Kim, Jang-Sub ; Serpedin, Erchin ; Shin, Dong-Ryeol

  • Author_Institution
    Texas A&M Univ., College Station
  • Volume
    15
  • fYear
    2008
  • fDate
    6/30/1905 12:00:00 AM
  • Firstpage
    87
  • Lastpage
    90
  • Abstract
    This letter proposes a new method to estimate the number of competing stations in IEEE 802.11 networks. Due to the nonlinear/non-Gaussian nature of measurement model, a nonlinear filtering algorithm, called the Gaussian mixture sigma point particle filter (GMSPPF), is proposed herein to estimate the number of competing stations. Since GMSPPF represents a better alternative to the conventional extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and unscented particle filter (UPF) for nonlinear/non-Gaussian (or Gaussian) tracking problems, we apply this filter for IEEE 802.11 WLANs. GMSPPF provides a more viable means for tracking in any conditions the number of competing stations in IEEE 802.11 WLANs relative to EKF, UKF, PF, and UPF. Further, GMSPPF presents both high accuracy as well as prompt reactivity to changes in the network occupancy status. For the more accurate method (GMSPPF), the combined access mode is shown to maximize the system throughput by switching between the basic access mode and the RTS/CTS access mode.
  • Keywords
    Gaussian processes; nonlinear filters; particle filtering (numerical methods); tracking; wireless LAN; Gaussian mixture sigma point particle filter; IEEE 802.11 networks; clear-to-send access mode; competing stations; measurement model; nonGaussian tracking problem; nonlinear filtering algorithm; nonlinear tracking problem; particle filtering-based estimation; request-to-send access mode; Filtering algorithms; Information filtering; Information filters; Internet; Media Access Protocol; Particle filters; Particle measurements; Particle tracking; State estimation; Throughput; Estimation; filtering; network;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2007.911182
  • Filename
    4418396