Abstract
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.
TRW and UCSD
This research was supported in part by the National Science Foundation under NSF grant CCR-9714651.
This work was performed while the author was with the Electrical & Computer Engineering Department of the University of California, San Diego.
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References
L. Kanal and A. Sastry. Models for Channels with Memory and Their Applications to Error Control. Proc. of the IEEE, vol. 66, pp. 724–744, July 1978.
E. Gilbert. Capacity of a burst-noise channel. Bell Systems Tech. Journal, vol. 39, pp. 1253–1266, Sept. 1960.
B. Fritchman. A Binary Channel Characterization Using Partitioned Markov Chains. IEEE Trans. on Info. Theory, vol. IT-13, pp. 221–227, Apr. 1967.
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.
S. Sivaprakasam and K. Shanmugan. An Equivalent Markov Model for Burst Errors in Digital Channels. IEEE Trans. on Comm., vol. COM-43, pp. 1347–1354, 1995.
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.
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.
C. Burke and M. Rosenblatt. A Markovian function of a Markov chain. Ann. of Math. Stat., vol. 29, 1958, pp. 1112–1122.
J. Hachgian. Collapsed Markov chain and the Chapman-Kolmogorov equation. Ann. of Math. Stat., vol. 34, 1963, pp. 233–237.
J. Kemeny and J. Snell. Finite Markov Chains. D. Van Nostrand Company, Inc., 1960.
G. Rubino and B. Sericola. On Weak Lumpability in Markov Chains. J. Appl. Prob., No. 26, pp. 446–457, 1989.
G. Rubino and B. Sericola. A finite characterization of weak lumpable Markov processes: Part I — The discrete-time case. Stochastic Processes Appl., vol. 38, pp. 195–204, 1991.
G. Rubino and B. Sericola. Sojourn Times in Finite Markov Processes. J. Appl. Prob., No. 27, pp. 744–756, 1989.
R. T. Rockafellar. Convex Analysis, Princeton Univ. Press, Princeton, NJ, 1970.
D. Sonderman. Comparing Semi-Markov Processes. Mathematics of Operations Research, vol. 5, No. 1, Feb. 1980.
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.
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Chen, A.M., Rao, R.R. (2002). Wireless Channel Models-Coping with Complexity. In: Ganesh, R., Pahlavan, K., Zvonar, Z. (eds) Wireless Multimedia Network Technologies. The International Series in Engineering and Computer Science, vol 524. Springer, Boston, MA. https://doi.org/10.1007/0-306-47330-5_15
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DOI: https://doi.org/10.1007/0-306-47330-5_15
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