Abstract
This paper proposes a particle filter framework to perform an online estimation of the unsaturated buffers of the stations in the IEEE 802.11 distributed coordination function (DCF) network. Using this framework, an access point can adapt flow control to its serving stations and configure related parameters dynamically, thus improving the system throughput and reducing the packet latency. Current research analyzing the unsaturated condition in the IEEE 802.11 DCF network is based on the steady-state model, whereas this proposed method is devoted to the dynamic estimation for the probability distribution of the unsaturated buffer in the stations, in either homogeneous or heterogeneous networks. This study also employs theoretical support from Bayesian inference to the particle-filtering algorithm. The estimation accuracy and effectiveness were evaluated via root mean square error (RMSE) and time complexity. Furthermore, we considered different network loads and convergence speeds in our analysis. Our analysis demonstrated that the proposed dynamic estimation scheme has greater awareness of the traffic changes in the varying wireless network when compared with the traditional static traffic model.
Published Version
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