Wireless Channel Models-Coping with Complexity

  • An Mei Chen
  • Ramesh R. Rao

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.

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References

  1. [1]
    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.MathSciNetCrossRefGoogle Scholar
  2. [2]
    E. Gilbert. Capacity of a burst-noise channel. Bell Systems Tech. Journal, vol. 39, pp. 1253–1266, Sept. 1960.Google Scholar
  3. [3]
    B. Fritchman. A Binary Channel Characterization Using Partitioned Markov Chains. IEEE Trans. on Info. Theory, vol. IT-13, pp. 221–227, Apr. 1967.MATHGoogle Scholar
  4. [4]
    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
  5. [5]
    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.MATHGoogle Scholar
  6. [6]
    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
  7. [7]
    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
  8. [8]
    C. Burke and M. Rosenblatt. A Markovian function of a Markov chain. Ann. of Math. Stat., vol. 29, 1958, pp. 1112–1122.MathSciNetGoogle Scholar
  9. [9]
    J. Hachgian. Collapsed Markov chain and the Chapman-Kolmogorov equation. Ann. of Math. Stat., vol. 34, 1963, pp. 233–237.Google Scholar
  10. [10]
    J. Kemeny and J. Snell. Finite Markov Chains. D. Van Nostrand Company, Inc., 1960.Google Scholar
  11. [11]
    G. Rubino and B. Sericola. On Weak Lumpability in Markov Chains. J. Appl. Prob., No. 26, pp. 446–457, 1989.Google Scholar
  12. [12]
    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.CrossRefMathSciNetMATHGoogle Scholar
  13. [13]
    G. Rubino and B. Sericola. Sojourn Times in Finite Markov Processes. J. Appl. Prob., No. 27, pp. 744–756, 1989.Google Scholar
  14. [14]
    R. T. Rockafellar. Convex Analysis, Princeton Univ. Press, Princeton, NJ, 1970.MATHGoogle Scholar
  15. [15]
    D. Sonderman. Comparing Semi-Markov Processes. Mathematics of Operations Research, vol. 5, No. 1, Feb. 1980.Google Scholar
  16. [16]
    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

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© Kluwer Academic Publishers 2002

Authors and Affiliations

  • An Mei Chen
  • Ramesh R. Rao

There are no affiliations available

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