Spatial statistics and models of spectrum use

In order to opportunistically exploit unused radio spectrum nodes of dynamic spectrum access (DSA) networks monitor the spectrum around them. Such cognitive radios can greatly benefit from a spatial characterization of spectrum use. However, there is need to find an efficient way to describe spatial use, something which has not been studied in details so far. In this paper, we introduce spatial statistics techniques as promising methods to describe spectrum use and enable optimization of DSA networks. We discuss two approaches to spatial modelling of spectrum, namely a deterministic approach based on a system model of the complete radio environment and an empirical approach that exploits passive measurements of the spectrum use. We elaborate on the impact of different network properties on the models and provide realistic parameter sets for generation of simulation scenarios. Additionally, we investigate cooperative sensing as a use case for spatial statistics based runtime optimization of the network configuration.

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

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

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

[4]  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.

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

[6]  F. Adachi,et al.  Impact of Shadowing Correlation on Spectrum E ffi ciency of a Power Controlled Cellular System , 2003 .

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

[8]  A. Wolisz,et al.  Primary Users in Cellular Networks: A Large-Scale Measurement Study , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[9]  Mohsen Guizani,et al.  Cognitive Radio Technology , 2006 .

[10]  Leif Hanlen,et al.  Strong Stochastic Stability for MANET Mobility Models , 2007, 2007 15th IEEE International Conference on Networks.

[11]  Amir Ghasemi,et al.  Asymptotic performance of collaborative spectrum sensing under correlated log-normal shadowing , 2007, IEEE Communications Letters.

[12]  Theodore S. Rappaport,et al.  Statistics of shadowing in indoor radio channels at 900 and 1900 MHz , 1992, MILCOM 92 Conference Record.

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

[14]  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..

[15]  Danijela Cabric,et al.  Experimental study of spectrum sensing based on energy detection and network cooperation , 2006, TAPAS '06.

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

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

[18]  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.

[19]  Danijela Cabric,et al.  White paper: Corvus: A cognitive radio approach for usage of virtual unlicensed spectrum , 2004 .

[20]  M. Petrova,et al.  Applications of Topology Information for Cognitive Radios and Networks , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[21]  M. Hata,et al.  Empirical formula for propagation loss in land mobile radio services , 1980, IEEE Transactions on Vehicular Technology.

[22]  T. Mattfeldt Stochastic Geometry and Its Applications , 1996 .

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

[24]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[25]  Janne Riihijärvi,et al.  Exploiting Spatial Statistics of Primary and Secondary Users towards Improved Cognitive Radio Networks , 2008, 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).

[26]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

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

[28]  Janne Riihijärvi,et al.  Spatial Statistics of Spectrum Usage: From Measurements to Spectrum Models , 2009, 2009 IEEE International Conference on Communications.

[29]  Adrian Baddeley,et al.  spatstat: An R Package for Analyzing Spatial Point Patterns , 2005 .

[30]  Bruce A. Fette,et al.  Cognitive Radio Technology , 2006 .

[31]  S.W. Ellingson,et al.  Spectral occupancy at VHF: implications for frequency-agile cognitive radios , 2005, VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005..

[32]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[33]  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..

[34]  Xianming Qing,et al.  Spectrum Survey in Singapore: Occupancy Measurements and Analyses , 2008, 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).

[35]  Brian L. Mark,et al.  Estimation of maximum interference-free power level for opportunistic spectrum access , 2009, IEEE Transactions on Wireless Communications.

[36]  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.

[37]  Zhe Jiang,et al.  Spatial Statistics , 2013 .

[38]  Kevin W. Sowerby,et al.  A Quantitative Analysis of Spectral Occupancy Measurements for Cognitive Radio , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[39]  Jung-Sun Um,et al.  Applying Radio Environment Maps to Cognitive Wireless Regional Area Networks , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[40]  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.

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

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

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

[44]  H. Tullberg,et al.  Sensor Selection for Cooperative Spectrum Sensing , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[45]  Brian L. Mark,et al.  Collaborative Opportunistic Spectrum Access in the Presence of Multiple Transmitters , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

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

[47]  R.W. Brodersen,et al.  Spectrum Sensing Measurements of Pilot, Energy, and Collaborative Detection , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[48]  D. Stoyan,et al.  Stochastic Geometry and Its Applications , 1989 .

[49]  Fortunato Santucci,et al.  A general correlation model for shadow fading in mobile radio systems , 2002, IEEE Communications Letters.