A second-order statistical method for spectrum sensing in correlated shadowing and fading environments

Spectrum sensing is one of the most important tasks of cognitive radios (CRs) in future wireless systems and of user equipments (UEs) in next generation wireless networks (NGWNs). Therefore, deciding whether a specific portion of radio frequency (RF) spectrum is occupied or not is of paramount importance for all sorts of future wireless communications systems. In this study, a spectrum sensing method that employs a second-order statistical approach is proposed for detecting fast fading signals in spatially correlated shadowing environments. Analysis and performance results are presented along with the discussion related to the performance comparison of energy detection method.

[1]  Gordon L. Stuber,et al.  Principles of Mobile Communication , 1996 .

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

[3]  Vladimir I. Kostylev,et al.  Energy detection of a signal with random amplitude , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[4]  Georgios B. Giannakis,et al.  A two-dimensional channel simulation model for shadowing processes , 2003, IEEE Trans. Veh. Technol..

[5]  John G. Proakis,et al.  Digital Communications , 1983 .

[6]  H. Tang,et al.  Some physical layer issues of wide-band cognitive radio systems , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[7]  M. Oner,et al.  Cyclostationarity based air interface recognition for software radio systems , 2004, Proceedings. 2004 IEEE Radio and Wireless Conference (IEEE Cat. No.04TH8746).

[8]  Jon W. Wallace,et al.  Improved Spectrum Sensing by Utilizing Signal Autocorrelation , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[9]  W. Marsden.  I and J , 2012 .

[10]  Jeffrey H. Reed,et al.  A new approach to signal classification using spectral correlation and neural networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[11]  Hüseyin Arslan,et al.  Binary Time Series Approach to Spectrum Prediction for Cognitive Radio , 2007, 2007 IEEE 66th Vehicular Technology Conference.

[12]  H. Urkowitz.  Energy detection of unknown deterministic signals , 1967 .

[13]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[14]  Anant Sahai,et al.  Fundamental design tradeoffs in cognitive radio systems , 2006, TAPAS '06.

[15]  James Roberts.  Logarithmic Detection with Postdetection Filtering , 1978, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Matthias Patzold,et al.  A deterministic digital simulation model for Suzuki processes with application to a shadowed Rayleigh land mobile radio channel , 1996 .

[17]  Andrea Goldsmith,et al.  Error statistics of real-time power measurements in cellular channels with multipath and shadowing , 1994 .

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

[19]  Mort Naraghi-Pour,et al.  Autocorrelation-Based Spectrum Sensing Algorithms for Cognitive Radios , 2008, 2008 Proceedings of 17th International Conference on Computer Communications and Networks.

[20]  Claude Oestges,et al.  Experimental Characterization and Modeling of Outdoor-to-Indoor and Indoor-to-Indoor Distributed Channels , 2010, IEEE Transactions on Vehicular Technology.

[21]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[22]  D. Owen.  Handbook of Mathematical Functions with Formulas , 1965 .