DocumentCode
674911
Title
Dynamic learning for cognitive radio sensing
Author
Seung-Jun Kim ; Giannakis, Georgios
Author_Institution
Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Minneapolis, MN, USA
fYear
2013
fDate
15-18 Dec. 2013
Firstpage
388
Lastpage
391
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.
Keywords
cognitive radio; dictionaries; learning (artificial intelligence); radiofrequency interference; signal detection; cognitive radio sensing; dictionary learning framework; dynamic learning; spatiotemporal interference power distribution; spectrum sensing; temporal dynamics; Customer relationship management; Dictionaries; Robustness; Single photon emission computed tomography; Stacking;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 IEEE 5th International Workshop on
Conference_Location
St. Martin
Print_ISBN
978-1-4673-3144-9
Type
conf
DOI
10.1109/CAMSAP.2013.6714089
Filename
6714089
Link To Document