Probabilistic Modeling of IEEE 802.11 Distributed Coordination Functions

TL;DR

This paper presents a new Markov model for IEEE 802.11 DCF that accurately predicts network behavior by incorporating detailed collision probability estimates and is validated against realistic simulations.

Contribution

The paper introduces a novel Markov model for IEEE 802.11 DCF that improves accuracy by detailed collision probability estimation and validation against simulations.

Findings

Model closely matches simulation results across various topologies

Accurate collision probability estimates enhance model precision

Product approximation effectively simplifies joint probability calculations

Abstract

We introduce and analyze a new Markov model of the IEEE 802.11 Distributed Coordination Function (DCF) for wireless networks. The new model is derived from a detailed DCF description where transition probabilities are determined by precise estimates of collision probabilities based on network topology and node states. For steady state calculations, we approximate joint probabilities from marginal probabilities using product approximations. To assess the quality of the model, we compare detailed equilibrium node states with results from realistic simulations of wireless networks. We find very close correspondence between the model and the simulations in a variety of representative network topologies.