Practical radio environment mapping with geostatistics

In this paper we present results from the first application of robust geostatistical modeling techniques to radio environment and coverage mapping of wireless networks. We perform our analysis of these methods with a case study mapping the coverage of a 2.5 GHz WiMax network at the University of Colorado, Boulder. Drawing from our experiences, we propose several new methods and extensions to basic geostatistical theory that are necessary for use in a radio mapping application. We also derive a set of best practices and discuss potential areas of future work. We find that this approach to radio environment mapping is feasible and produces maps that are more accurate and informative than both explicitly tuned path loss models and basic data fitting approaches.

[1]  Janne Riihijärvi,et al.  Characterization and modelling of spectrum for dynamic spectrum access with spatial statistics and random fields , 2008, 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications.

[2]  Emiliano Dall'Anese.  Geostatistics-inspired sparsity-aware cooperative spectrum sensing for cognitive radio networks , 2010, MobiOpp '10.

[3]  J. Wiart,et al.  Geostatistical interpolation for mapping radio-electric exposure levels , 2006, 2006 First European Conference on Antennas and Propagation.

[4]  Evangelos A. Yfantis,et al.  Efficiency of kriging estimation for square, triangular, and hexagonal grids , 1987 .

[5]  W.C.Y. Lee,et al.  Estimate of local average power of a mobile radio signal , 1985, IEEE Transactions on Vehicular Technology.

[6]  David Wetherall,et al.  Predictable 802.11 packet delivery from wireless channel measurements , 2010, SIGCOMM '10.

[7]  Pierre Goovaerts,et al.  Second-Phase Sampling Designs for Non-Stationary Spatial Variables. , 2009, Geoderma.

[8]  David A. Cohn,et al.  Active Learning with Statistical Models , 1996, NIPS.

[9]  Mikhail Kanevski.  Advanced Mapping of Environmental Data , 2008 .

[10]  Multivariate Geostatistics , 2004 .

[11]  Edward W. Knightly,et al.  Assessment of urban-scale wireless networks with a small number of measurements , 2008, MobiCom '08.

[12]  Suman Banerjee,et al.  Airshark: detecting non-WiFi RF devices using commodity WiFi hardware , 2011, IMC '11.

[13]  Janne Riihijärvi,et al.  Spatial statistics and models of spectrum use , 2009, Comput. Commun..

[14]  Richardo A. Olea,et al.  Semivariogram modeling by weighted least squares , 1996 .

[15]  Janne Riihijärvi,et al.  Influence of transmitter configurations on spatial statistics of radio environment maps , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[16]  Aleksandar Neskovic,et al.  Indoor electric field level prediction model based on the artificial neural networks , 2000, IEEE Communications Letters.

[17]  Robert Haining,et al.  Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95 , 1993 .

[18]  Noel A Cressie,et al.  Statistics for Spatial Data, Revised Edition. , 1994 .

[19]  Ricardo A. Olea,et al.  A six-step practical approach to semivariogram modeling , 2006 .

[20]  Newbury,et al.  Logarithmic 3-Band Color Encoding: Robust Method for Display and Comparison of Compositional Maps in Electron Probe X-ray Microanalysis. , 1999, Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada.

[21]  Yong Liu,et al.  A toolkit for automatically constructing outdoor radio maps , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[22]  David Wetherall,et al.  Tool release: gathering 802.11n traces with channel state information , 2011, CCRV.

[23]  F. Landstorfer,et al.  Dominant Path Prediction Model for Urban Scenarios , 2005 .

[24]  Vinay Kolar,et al.  Enhancing cognitive radios with spatial statistics: From radio environment maps to topology engine , 2009, 2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[25]  WetherallDavid,et al.  Predictable 802.11 packet delivery from wireless channel measurements , 2010 .

[26]  J. W. Groenigen,et al.  Constrained optimisation of soil sampling for minimisation of the kriging variance , 1999 .

[27]  Dirk Grunwald,et al.  Bounding the Practical Error of Path Loss Models , 2012 .

[28]  A. Oiea.  Sampling Design Optimization for Spatial Functions , 2004 .

[29]  Dirk Grunwald,et al.  Bounding the error of path loss models , 2011, 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[30]  Abdullah Konak,et al.  A kriging approach to predicting coverage in wireless networks , 2009 .