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Analysis and simulation of wireless signal propagation applying geostatistical interpolation techniques

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper is a part of an ongoing research to examine the capability of geostatistical analysis for mobile networks coverage prediction, simulation and tuning. Mobile network coverage predictions are used to find network coverage gaps and areas with poor serviceability. They are essential data for engineering and management in order to make better decision regarding rollout, planning and optimisation of mobile networks.The objective of this research is to evaluate different interpolation techniques in coverage prediction. In method presented here, raw data collected from drive testing a sample of roads in study area is analysed and various continuous surfaces are created using different interpolation methods. Two general interpolation methods are used in this paper with different variables; first, Inverse Distance Weighting (IDW) with various powers and number of neighbours and second, ordinary kriging with Gaussian, spherical, circular and exponential semivariogram models with different number of neighbours. For the result comparison, we have used check points coming from the same drive test data. Prediction values for check points are extracted from each surface and the differences with actual value are computed.The output of this research helps finding an optimised and accurate model for coverage prediction.
Rocznik
Tom
Strony
261--270
Opis fizyczny
Bibliogr. 15 poz.
Twórcy
autor
  • Dept. of Surveying and Geomatics Eng., College of Eng., University of Tehran
  • MTN-Irancell- Network Group- Radio Division- GIS Team
autor
  • Dept. of Surveying and Geomatics Eng., College of Eng., University of Tehran
  • MTN-Irancell- Network Group- Radio Division- GIS Team
autor
  • Dept. of Surveying and Geomatics Eng., College of Eng., University of Tehran
  • MTN-Irancell- Network Group- Radio Division- GIS Team
Bibliografia
  • 1.Arpee J., Herndon, Stan Gutowski, Mustafa Touati, 2000, “Apparatus and method for geostatistical analysis of wireless signal propagation”, US patent 6,711,404,2004, Technical Aspect of Geostatistics, pp. 2-1 to 2-17
  • 2. Armstrong M., 1998, Basic linear geostatistics
  • 3.Barnes, R. 2003. “Variogram tutorial” Golden, CO: Golden. Software. Available online at http://www.goldensoftware.com/. variogramTutorial.pdf
  • 4.Cressie, N., 1993, Statistics for spatial data, Wiley Interscience
  • 5.O’Sullivan D., and David J. Unwin, 2002 “Geographic Information Analysis”, Willy Interscience
  • 6.Krige, D. G. 1951. A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the Chemical, Metallurgical and Mining Society of South Africa 52: 119_139.
  • 7.Konak, A., 2009, “A kriging approach to predicting coverage in wireless networks”, International Journal of Mobile Network Design and Innovation 3: 64-70.
  • 8.Konak, A., 2010, “Estimation path loss in wireless local area using ordinary”, Proceedings of the 2010 Winter Simulation Conference, B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.
  • 9.Krivoruchko K., 2001, “Using linear and non-linear kriging interpolators to produce probability maps”, Annual conference of the international association for mathematical Geology, Cancun, Mexico, September, IAMG 2001
  • 10.Matheron G., 1963 “principles of geostatistics,” , Economic Geology; 1 December 1963; v. 58; no. 8; p. 1246-1266; DOI: 10.2113/gsecongeo.58.8.1246)
  • 11.Robert H. P., 1993, Spatial data analysis in the social and environmental sciences
  • 12.Wackernagel H., 2003, Multivariate geostatistics: an introduction with applications
  • 13.Webster R., T. M. Burgess, 2006, “Optimal interpolation and isarithmic mapping of soil properties III changing drift and universal kriging”, Soil Science, Vol. 31, Issue 3, pp. 505–524
  • 14.Yoo S., 1994, “A spatial prediction theory for long-term fading in mobile radio communications”, ETRI Journal, Vol. 15, No ¾, Jan 1994
  • 15.Zimmerman DL, Zimmerman M. B., 1991, “A comparison of spatial semivariogram estimators and corresponding ordinary kriging predictors”, Technometrics, Vol. 33, pp. 77-91
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f2fadb63-5060-4882-90a3-362a5074e9f7
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