Spectrum sensing for cognitive radios using Kriged Kalman filtering

Seung Jun Kim, Emiliano Dall'Anese, Georgios B Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A cooperative spectrum sensing algorithm for cognitive radios (CRs) is developed using the novel notion of channel gain maps. These maps capture the spatio-temporal variation of the RF propagation in the geographical area where the CR network is operated. They are tracked via Kriged Kalman filtering (KKF), a tool with well-appreciated merits in geo-statistics. This in turn enables the activity of an unknown number of primary users to be tracked using a sparse regression technique based on a weighted least-squares criterion regularized by the ℓ1 norm of the regression coefficient vector. Simulations demonstrate considerable performance advantage of the proposed scheme over a crude path loss-based sensing algorithm.

Original languageEnglish (US)
Title of host publicationCAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Pages392-395
Number of pages4
DOIs
StatePublished - Dec 1 2009
Event2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009 - Aruba, Netherlands
Duration: Dec 13 2009Dec 16 2009

Publication series

NameCAMSAP 2009 - 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

Other

Other2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009
CountryNetherlands
CityAruba
Period12/13/0912/16/09

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