A unscented particle filtering approach to estimating competing stations in IEEE 802.11 WLANs

The number of competing stations has great impact on the network performance of wireless LANs. It is therefore of great interest to obtain accurate estimation of the number of competing stations so that adaptive control mechanisms can be carried out accordingly. Based on the observation that this estimation problem is nonlinear/non-Gaussian in nature, we propose to use a sequential Monte Carlo technique, namely, the particle filtering to improve the estimation accuracy. One key step in the proposed scheme is to exploit the unscented particle filter, which combines the merits of unscented transformation and particle filtering. Our simulation results indicate that the unscented particle filter can increase the accuracy of the estimation upto 33% in terms of the root mean square error (RMSE), compared with the Extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the SIR-particle filter.