A Markov-based channel model algorithm for wireless networks
Wireless Networks
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
Low complexity channel models for approximating flat rayleigh fading in network simulations
Low complexity channel models for approximating flat rayleigh fading in network simulations
Modeling frame-level errors in GSM wireless channels
Performance Evaluation - Internet performance symposium (IPS 2002)
Link-level measurements from an 802.11b mesh network
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Linear-complexity models for wireless MAC-to-MAC channels
Wireless Networks
Markov and multifractal wavelet models for wireless MAC-to-MAC channels
Performance Evaluation
Hidden Markov modeling of flat fading channels
IEEE Journal on Selected Areas in Communications
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Accurate simulation and analysis of wireless networks are inherently dependent on accurate models which are able to provide real-time channel characterization. High-order Markov chains are typically used to model errors and losses over wireless channels. However, complexity (i.e., the number of states) of a high-order Markov model increases exponentially with the memory-length of the underlying channel. In this paper, we present a novel graph-theoretic methodology that uses Hamiltonian circuits to reduce the complexity of a high-order Markov model to a desired state budget. We also demonstrate the implication of unused states in complexity reduction of higher order Markov model. Our trace-driven performance evaluations for real wireless local area network (WLAN) and wireless sensor network (WSN) channels demonstrate that the proposed Hamiltonian Model, while providing orders of magnitude reduction in complexity, renders an accuracy that is comparable to the Markov model and better than the existing reduced state models.