Performance of Markov models for frame-level errors in IEEE 802.11 wireless LANs

Interference among different wireless hosts is becoming a serious issue due to the growing number of wireless LANs based on the popular IEEE 802.11 standard. Thus, an accurate modeling of error paths at the data link layer is indispensable for evaluating system performance and for tuning and optimizing protocols at higher layers. Error paths are usually described looking at sequences of consecutive correct or erroneous frames and at the distributions of their sizes. In recent years, a number of Markov-based stochastic models have been proposed in order to statistically characterize these distributions. Nevertheless, when applied to analyze the data traces we collected, they exhibit several flaws. In this paper, to overcome these model limitations, we propose a new algorithm based on a semi-Markov process, where each state characterizes a different error pattern. The model has been validated by using measures from a real environment. Moreover, we have compared our method with other promising models already available in the literature. Numerical results show that our proposal performs better than the other models in capturing the long-term temporal correlation of real measured traces. At the same time, it is able to estimate first-order statistics with the same accuracy of the other models, but with a minor computational complexity.