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 language | English (US) |
|---|---|
| Title of host publication | 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 |
| Pages | 388-391 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Dec 1 2013 |
| Event | 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 - Saint Martin, France Duration: Dec 15 2013 → Dec 18 2013 |
Publication series
| Name | 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 |
|---|
Other
| Other | 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 |
|---|---|
| Country | France |
| City | Saint Martin |
| Period | 12/15/13 → 12/18/13 |
Fingerprint Dive into the research topics of 'Dynamic learning for cognitive radio sensing'. Together they form a unique fingerprint.
Cite this
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