TY - GEN
T1 - Dynamic learning for cognitive radio sensing
AU - Kim, Seung Jun
AU - Giannakis, Georgios B.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84894201101&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894201101&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2013.6714089
DO - 10.1109/CAMSAP.2013.6714089
M3 - Conference contribution
AN - SCOPUS:84894201101
SN - 9781467331463
T3 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
SP - 388
EP - 391
BT - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
T2 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Y2 - 15 December 2013 through 18 December 2013
ER -