Optimizing IEEE 802.11 DCF using Bayesian estimators of the network state

The optimization mechanisms proposed in the literature for the distributed coordination function (DCF) of the IEEE 802.11 protocol are often based on adapting the backoff parameters to the estimate of the number of competing terminals in the network. However, existing estimation algorithms are either inaccurate or too complex. In this paper we propose an enhanced version of the IEEE 802.11 DCF that employs an estimator of the number of competing terminals based on a sequential Monte Carlo (SMC) or a approximate maximum a posteriori (MAP) approach. The algorithm uses a Bayesian framework, optimizing the backoff parameters of the DCF based on the predictive distribution of the number of competing terminals. We show that our algorithm is simple yet highly accurate even at small time scales. We implement our proposed new DCF in the ns-2 simulator and show that it outperforms existing methods. We also show that its accuracy can be used to improve the results of the protocol even when the nodes are not in saturation mode.