Control mechanisms for packet audio in the internet
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 1
Markov-based modeling of wireless local area networks
MSWIM '03 Proceedings of the 6th ACM international workshop on Modeling analysis and simulation of wireless and mobile systems
Linear-complexity models for wireless MAC-to-MAC channels
Wireless Networks
Loss performance model for wireless channels with autocorrelated arrivals and losses
Computer Communications
Cross-layer modeling of wireless channels for data-link and IP layer performance evaluation
Computer Communications
Accurate hidden Markov modeling of packet losses in indoor 802.11 networks
IEEE Communications Letters
Design and implementation of real-time betting system with offline terminals
Electronic Commerce Research and Applications
An error control scheme with virtually segmented packets for wireless multicast protocols
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Simple, accurate and computationally efficient wireless channel modeling algorithm
WWIC'05 Proceedings of the Third international conference on Wired/Wireless Internet Communications
Survey: Performance models for wireless channels
Computer Science Review
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In this paper, we analyze the errors observed at the link layer of an 802.11b network. Our analysis at all supported bitrates (i.e., 2, 5.5. and 11 Mbps) establishes that the error patterns are not memoryless, and therefore, they exhibit a certain level of temporal dependencies. Thus, we evaluate the suitability of a two-state Markov model to capture the channel behavior. Non-stationarity of the error patterns renders such a simplistic model inadequate, and hence, we consider higher order models. This formulates a key contribution of this paper, and that is, a hierarchical Markov model, which captures the non-stationarity of the channel while employing real-time application-specific considerations to determine state-transition probabilities.