Optimizing IEEE 802.11 DCF Using Bayesian Estimators of the Network State


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Date: January 2008
Type: White Paper
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Overview: 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. This paper proposes 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.

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