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
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