Wireless Channel Models-Coping with Complexity
In this work we explore two techniques to capture the behavior of wireless channels with mathematically tractable models. The first technique involves state-space aggregation to reduce a large number of states of a Markov chain to a fewer number of states. The property of strong and weak lumpability is discussed. The second technique involves stochastic bounding. These techniques are applied to three different previously published wireless channel models: mobile VHF, wireless indoor, and Rayleigh fading channels. Results show that our stochastic bounding technique can produce simple yet useful upper bounds for the original channel model. We investigate the goodness of these bounds through the performance of higher-layer error control protocols such as stop-and-go and TCP.
KeywordsMarkov Chain Hazard Rate Channel Model Sojourn Time Probability Transition Matrix
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- E. Gilbert. Capacity of a burst-noise channel. Bell Systems Tech. Journal, vol. 39, pp. 1253–1266, Sept. 1960.Google Scholar
- F. Swarts and H. Ferreira. Markov Characterization of Digital Fading Mobile VHF Channels. IEEE Trans. on Comm., vol. COM-43, pp. 997–985, Mov. 1994.Google Scholar
- H. Wang and N. Moayeri. Finite-State Markov Channel — A Useful Model for Radio Communication Channels. IEEE Trans. on Veh. Tech., vol. VT-44, pp. 163–171, Feb. 1995.Google Scholar
- M. Zorzi, R. Rao, and L. Milstein. On the Accuracy of a First-Order Markov Model for Data Block Transmission on Fading Channels. Proc. ICUPC’95, pp. 221–215.Google Scholar
- J. Hachgian. Collapsed Markov chain and the Chapman-Kolmogorov equation. Ann. of Math. Stat., vol. 34, 1963, pp. 233–237.Google Scholar
- J. Kemeny and J. Snell. Finite Markov Chains. D. Van Nostrand Company, Inc., 1960.Google Scholar
- G. Rubino and B. Sericola. On Weak Lumpability in Markov Chains. J. Appl. Prob., No. 26, pp. 446–457, 1989.Google Scholar
- G. Rubino and B. Sericola. Sojourn Times in Finite Markov Processes. J. Appl. Prob., No. 27, pp. 744–756, 1989.Google Scholar
- D. Sonderman. Comparing Semi-Markov Processes. Mathematics of Operations Research, vol. 5, No. 1, Feb. 1980.Google Scholar
- M. Zorzi and R. R. Rao On the Statistics of Block Errors in Bursty Channels. IEEE sTransaction on Communications, Vol. 45, No. 6, Jun. 1997.Google Scholar