Dynamic learning for cognitive radio sensing

Seung Jun Kim, Georgios B. Giannakis

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

1 Scopus citations

Abstract

Spectrum sensing algorithms for cognitive radios that can interpolate and predict the spatio-temporal interference power distribution are proposed using the dictionary learning framework. The algorithms jointly estimate the dictionaries to capture the spatial spectrum measurements as well as their temporal dynamics via parsimoniously chosen atoms. Both batch and efficient online implementations are developed. Numerical tests verify the effectiveness of the novel approach.

Original languageEnglish (US)
Title of host publication2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Pages388-391
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 - Saint Martin, France
Duration: Dec 15 2013Dec 18 2013

Publication series

Name2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

Other

Other2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
CountryFrance
CitySaint Martin
Period12/15/1312/18/13

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    Kim, S. J., & Giannakis, G. B. (2013). Dynamic learning for cognitive radio sensing. In 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 (pp. 388-391). [6714089] (2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013). https://doi.org/10.1109/CAMSAP.2013.6714089