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
Link To Document :
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