Spatial Statistics of Spectrum Usage: From Measurements to Spectrum Models

Several measurement studies have found a large amount of underutilized radio spectrum. More flexible regulation employing dynamic spectrum access (DSA) has been proposed as solution to this problem. The analysis of several aspects of DSA systems, e.g., cooperative sensing, requires good spatial models of spectrum usage. However, only very focused models such as propagation or shadowing correlation models exist. In this paper we apply techniques developed by the spatial statistics community to the modelling of spectrum. In more detail, we use random fields and the semivariogram to describe the spatial correlation of spectrum usage.We extract parameters from extensive real-life measurements for multiple wireless technologies. These parameter sets enable other researchers to use the model for different tasks ranging from theoretical to simulation-based studies.

[1]  A. Gelfand,et al.  Bayesian Variogram Modeling for an Isotropic Spatial Process , 1997 .

[2]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[3]  Ainslie,et al.  CORRELATION MODEL FOR SHADOW FADING IN MOBILE RADIO SYSTEMS , 2004 .

[4]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[5]  Konstantinos Psounis,et al.  Modeling spatially correlated data in sensor networks , 2006, TOSN.

[6]  Brian L. Mark,et al.  Estimation of Interference-Free Transmit Power for Opportunistic Spectrum Access , 2008, 2008 IEEE Wireless Communications and Networking Conference.

[7]  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.

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

[9]  Petri Mähönen,et al.  Evaluation of Spectrum Occupancy in Indoor and Outdoor Scenario in the Context of Cognitive Radio , 2007, 2007 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[10]  R. Lark.  Optimized spatial sampling of soil for estimation of the variogram by maximum likelihood , 2002 .

[11]  Anant Sahai,et al.  Cooperative Sensing among Cognitive Radios , 2006, 2006 IEEE International Conference on Communications.

[12]  P. Mahonen,et al.  Evaluation of Cooperative Spectrum Sensing Based on Large Scale Measurements , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[13]  G. Matheron.  Principles of geostatistics , 1963 .

[14]  N. Cressie.  Fitting variogram models by weighted least squares , 1985 .

[15]  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 .

[16]  Maliha S. Nash,et al.  Spatial Statistics and Computational Methods , 2004, Technometrics.

[17]  Anant Sahai,et al.  What is a Spectrum Hole and What Does it Take to Recognize One? , 2009, Proceedings of the IEEE.

[18]  Jesper Møller,et al.  Spatial statistics and computational methods , 2003 .

[19]  E. Visotsky,et al.  On collaborative detection of TV transmissions in support of dynamic spectrum sharing , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[20]  A. Ghasemi,et al.  Collaborative spectrum sensing for opportunistic access in fading environments , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..