Name
Title | ||
|---|---|---|
A markov chain model for coarse timescale channel variation in an 802.16e wireless network |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/INFCOM.2012.6195553 | international conference on computer communications |
Keywords | Field | DocType |
channel model,wireless network,abstract model,mc transition matrix,finite-state markov chain,transition matrix,empirical approach,wireless channel variation,markov chain model,markov chain,coarse timescale channel variation,transition probability,parsimonious modeling approach,autocorrelation function,markov processes,predictive models,broadband networks,correlation,steady state,prediction model,shadow mapping,wimax,log normal distribution,signal strength,random variable,markov process | Statistical physics,Markov process,Stochastic matrix,Continuous-time Markov chain,Simulation,Markov model,Computer science,Markov chain,Balance equation,Variable-order Markov model,Distributed computing,Autocorrelation | Conference |
ISSN | Citations | PageRank |
0743-166X | 4 | 0.39 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anand Seetharam | 1 | 188 | 18.94 |
| Jim Kurose | 2 | 5307 | 610.06 |
| Dennis Goeckel | 3 | 1060 | 69.96 |
| Gautam Bhanage | 4 | 178 | 19.09 |