Advertisement

Traffic Modeling and Characterization for UTRAN

  • Xi Li
  • Su Li
  • Carmelita Görg
  • Andreas Timm-Giel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3970)

Abstract

This paper presents an analytical approach for characterizing the aggregated traffic carried in the UMTS Terrestrial Radio Access Network (UTRAN). The characteristic of the incoming traffic stream of UTRAN is studied based on the measured trace traffic from the simulations. The main idea of the aggregated traffic modelling is to employ Batch Markov Arrival Process (BMAP) model as an analytically tractable model, which considers different lengths of packets and batch arrivals. In this paper, the setup and customization of the BMAP model for characterizing the aggregated traffic in UTRAN is presented. The accuracy of the BMAP model is demonstrated by comparing with simulations and Poisson traffic model. At the end the potential application of the presented approach and its advantages is briefly discussed.

Keywords

Medium Access Control Traffic Modeling Universal Mobile Telecommunication System Traffic Stream Transmission Time Interval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Klemm, A., Lindemann, C., Lohmann, M.: Traffic Modeling and Characterization for UMTS Networks. In: Proc. of the Globecom, Internet Performance Symposium, San Antonio TX, November 2001, pp. 1741–1746 (2001)Google Scholar
  3. 3.
    Riska, A.: Aggregate Matrix-analytic Techniques and their ApplicationsGoogle Scholar
  4. 4.
    Lucantoni, D.M.: New Results on the Single Server Queue with a Batch Markovian Arrival Process. Comm. in Statistics: Stochastic Models 7, 1–46 (1991)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Kang, S.H., Kim, Y.H., Sung, D.K., Choi, B.D.: An application of Markovian Arrival Process (MAP) to modeling superposed ATM cell streams. IEEE Trans. Commun. 50(4), 633–642 (2002)CrossRefGoogle Scholar
  6. 6.
    Ryden, T.: An EM Algorithm for Parameter Estimation in Markov Modulated Poisson Processes. Computational Statistics and Data Analysis 21, 431–447 (1996)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Breuer, L.: An EM algorithm for Batch Markovian Arrival Processes and its comparison to a simpler estimation procedure. Annals of Operations Research 112, 123–138 (2002)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Lindemann, B., Lohmann, M.: Numerical Robust Parameter Estimation for the Batch Markovian Arrival Process Using RandomizationGoogle Scholar
  9. 9.
    Klemm, A., Lindemann, C., Lohmann, M.: Modeling IP Traffic Using the Batch Markovian Arrival Process (extended version). Performance Evaluation 54, 149–173 (2003)CrossRefGoogle Scholar
  10. 10.
    Willinger, W., Paxson, V., Taqqu, M.S.: Self-similarity and Heavy Tails: Structural Modeling of Network Traffic. In: A Practical Guide to Heavy Tails, pp. 27–53. Chapman &Hall, Boca Raton (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xi Li
    • 1
  • Su Li
    • 1
  • Carmelita Görg
    • 1
  • Andreas Timm-Giel
    • 1
  1. 1.Communication NetworksUniversity of Bremen,FB1BremenGermany

Personalised recommendations