Skip to main content
SpringerLink
Log in

Microcell prediction model based on support vector machine algorithm

  • Published:
annals of telecommunications - annales des télécommunications Aims and scope Submit manuscript

Abstract

A new microcell prediction model for mobile radio environment is presented in this paper. The popular support vector machine algorithm is used as an optimizing tool to build a model. In order to validate the model quality, extensive electric field strength measurements were carried out in the city of Belgrade, for two different test transmitter locations. The analysis of the model has shown that proposed model is fast, accurate (on the order of the local mean measurements uncertainty), reliable, and suitable for computer implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Lee WCY (1993) Mobile communications design fundamentals. Wiley, New York

    Book  Google Scholar 

  2. Andersen JB, Rappaport TS, Yoshida S (1995) Propagation measurements and models for wireless communications channels. IEEE Commun Mag 33(1):42–49

    Article  Google Scholar 

  3. Barbiroli M, Carciofi C, Falciasecca G, Frullone M, Grazioso P, Varini A (2002) A new statistical approach for urban environment propagation modeling. IEEE Trans Veh Technol 51(5):1234–1241

    Article  Google Scholar 

  4. Rodriguez JV, Martinez SG, Garcia-Pardo C, Molina-Garcia-Pardo JM, Juan-Llancer L (2009) Comparison of various urban radiowave propagation models with measurements. IEEE Antenn Wirel Propag Lett 8:977–980

    Article  Google Scholar 

  5. Corre Y, Lostanlen Y (2009) Three-dimensional urban EM wave propagation model for radio network planning and optimization over large areas. IEEE Trans Veh Technol 58(7):3112–3123

    Article  Google Scholar 

  6. Rizk K, Wagen JF, Gardiol F (1997) Two-dimensional ray-tracing modeling for propagation prediction in microcellular environment. IEEE Trans Veh Technol 46(2):508–518

    Article  Google Scholar 

  7. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    MATH  Google Scholar 

  8. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, New York

    Book  Google Scholar 

  9. Lee WCY, Lee DJY (1998) Microcell prediction in dense urban area. IEEE Trans Veh Technol 47(1):246–253

    Article  Google Scholar 

  10. Gunn, S (1998) Support vector machines for classification and regression. Technical Report, School of Electronics and Computer Science, University of Southampton

  11. http://www.isis.ecs.soton.ac.uk/resources/svminfo/

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, P. O. Box 3554, 11120, Belgrade, Serbia

    Vladimir Slavkovic, Aleksandar Neskovic & Natasa Neskovic

Authors
  1. Vladimir Slavkovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aleksandar Neskovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Natasa Neskovic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladimir Slavkovic.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Slavkovic, V., Neskovic, A. & Neskovic, N. Microcell prediction model based on support vector machine algorithm. Ann. Telecommun. 69, 123–129 (2014). https://doi.org/10.1007/s12243-013-0356-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-013-0356-9

Keywords

Search

Navigation