Estimating contention of IEEE 802.11 broadcasts based on inter-frame idle slots

Recent advances in communication technology has enabled vehicles to communicate with each other autonomously through the use of IEEE 802.11p protocol. Vehicle-to-vehicle communication regularly makes use of the broadcast mode transmissions, which are not often used prior to this application. Broadcast mode transmissions are more prone to frame loss from channel contention than unicasts due to its inability to adapt, and are unable to recover lost frames. This makes them very sensitive to channel congestion. In this paper, we first apply a variant of Bianchi et al.'s Markov model of the Distributed Coordination Function (DCF), to show that the observed inter-frame idle slots can be expressed as a probability distribution conditional on the number of saturated stations. It therefore follows that the probability distribution for the number of saturated stations can be estimated from inter-frame idle slots through Bayes Law. Second, we present a novel passive channel congestion estimation technique that observes the inter-frame idle slot counts in any given IEEE 802.11 network and uses a naïve Bayes classifier to estimate the current channel contention in terms of the number of concurrent saturated stations. This technique is evaluated using computer simulations, and is shown to produce more accurate estimates with faster convergence time than the existing technique of observing collision probability using channel busy status as a proxy.