a markov chain model for coarse timescale channel variation in an 802.16e wireless network
We are developing a finite-state Markov Chain (MC) channel model to capture wireless channel variations due to shadowing, which occur at a time scale of seconds. The Markov Chain is constructed by partitioning the entire range of shadowing into a finite number of intervals. We determine the MC transition matrix in two ways: (i) via a parsimonious modeling approach in which shadowing effects are modeled as a log-normally distributed random variable affecting the received power, and the transition probabilities are derived as functions of the variance and autocorrelation function of shadowing; (ii) via an empirical approach, in which the MC transition matrix is calculated by directly measuring the changes in signal strengths collected in a 802.16e (WiMAX) network. We present a validation of the abstract model by comparing its steady state and transient performance predictions with those computed using the empirically derived transition matrix.
shadowing wimax