Integration of heterogeneous spectrum sensing devices towards accurate REM construction

Spectrum sensing is a fundamental feature of cognitive radio technologies facilitating detection and localization of spectrum opportunities. Combined with centralized database storage, it fosters reliable spatial spectrum measurements and Radio Environmental Maps (REMs) construction that can provide an accurate insight in the spectrum utilization over time, space and frequency. The spectrum sensing process often combines different devices with different capabilities. This paper introduces novel REM construction platform that integrates several mid- and low-end spectrum sensing devices into a single heterogeneous testbed. The devices of interest are USRP2s, TI RF2500 and Sun SPOTs, which are upgraded with custom developed software for providing versatile spectrum sensing capabilities. The paper evaluates their performances and discusses their integration into a single heterogeneous testbed platform. Additionally, the paper evaluates the realized heterogeneous spectrum sensing testbed in terms of accurate REM derivation and additional statistics gathering for indoor environments.

[1]  Robert J. Renka,et al.  Multivariate interpolation of large sets of scattered data , 1988, TOMS.

[2]  Daniel Denkovski,et al.  Practical assessment of RSS-based localization in indoor environments , 2012, MILCOM 2012 - 2012 IEEE Military Communications Conference.

[3]  Erik G. Larsson,et al.  Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances , 2012, IEEE Signal Processing Magazine.

[4]  Janne Riihijärvi,et al.  Reliability of a radio environment Map: Case of spatial interpolation techniques , 2012, 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM).

[5]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[6]  Daniel Denkovski,et al.  Algorithms and bounds for energy-based multi-source localization in log-normal fading , 2012, 2012 IEEE Globecom Workshops.

[7]  Joanne E McEntee Information is available , 2008 .

[8]  Daniel Denkovski,et al.  Novel Policy Reasoning Architecture for Cognitive Radio Environments , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[9]  M. F. Hutchinson,et al.  Interpolating Mean Rainfall Using Thin Plate Smoothing Splines , 1995, Int. J. Geogr. Inf. Sci..

[10]  Kang G. Shin,et al.  Cognitive radios for dynamic spectrum access: from concept to reality , 2010, IEEE Wireless Communications.

[11]  D. Shepard A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[12]  I. A. Nalder,et al.  Spatial interpolation of climatic Normals: test of a new method in the Canadian boreal forest , 1998 .

[13]  Jianfeng Wang,et al.  Emerging cognitive radio applications: A survey , 2011, IEEE Communications Magazine.

[14]  Liljana Gavrilovska,et al.  Spectrum Sensing Framework for Cognitive Radio Networks , 2011, Wirel. Pers. Commun..

[15]  Valentin Rakovic,et al.  Constructing radio environment maps with heterogeneous spectrum sensors , 2011, 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[16]  Mahesh Sooriyabandara,et al.  Deployment and interface design considerations for Radio Environment Maps , 2012, 2012 IEEE 8th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[17]  Janne Riihijärvi,et al.  REM-enabled opportunistic LTE in the TV band , 2012, 2012 IEEE International Symposium on Dynamic Spectrum Access Networks.

[18]  M. Di Benedetto,et al.  Cognitive Radio and Networking for Cooperative Coexistence of Heterogeneous Wireless Networks , 2012, 2012 IEEE First AESS European Conference on Satellite Telecommunications (ESTEL).

[19]  Daniel Denkovski,et al.  HOS Based Goodness-of-Fit Testing Signal Detection , 2012, IEEE Communications Letters.

[20]  Gwo-Fong Lin,et al.  A spatial interpolation method based on radial basis function networks incorporating a semivariogram model , 2004 .

[21]  Jeffrey H. Reed,et al.  Performance Evaluation of Radio Environment Map-Enabled Cognitive Spectrum-Sharing Networks , 2007, MILCOM 2007 - IEEE Military Communications Conference.

[22]  Zhe Chen,et al.  Demonstration of real-time spectrum sensing for cognitive radio , 2010, 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE.

[23]  T. Aaron Gulliver,et al.  On the construction of Radio Environment Maps for Cognitive Radio Networks , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[24]  Keping Long,et al.  Self-organization paradigms and optimization approaches for cognitive radio technologies: a survey , 2013, IEEE Wireless Communications.