TY - GEN
T1 - Cognitive radio spectrum prediction using dictionary learning
AU - Kim, Seung Jun
AU - Giannakis, Georgios B.
PY - 2013
Y1 - 2013
N2 - Spatio-temporal spectrum prediction algorithms for cognitive radios (CRs) are developed using the framework of dictionary learning and compressive sensing. The interference power levels at each CR node locations are predicted using the measurements from a subset of CR nodes without a priori knowledge on the primary transmitters. Batch and online alternatives are presented, where the online algorithm features low complexity and memory requirements. Numerical tests verify the performance of the proposed novel methods.
AB - Spatio-temporal spectrum prediction algorithms for cognitive radios (CRs) are developed using the framework of dictionary learning and compressive sensing. The interference power levels at each CR node locations are predicted using the measurements from a subset of CR nodes without a priori knowledge on the primary transmitters. Batch and online alternatives are presented, where the online algorithm features low complexity and memory requirements. Numerical tests verify the performance of the proposed novel methods.
UR - http://www.scopus.com/inward/record.url?scp=84904106737&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904106737&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2013.6831565
DO - 10.1109/GLOCOM.2013.6831565
M3 - Conference contribution
AN - SCOPUS:84904106737
SN - 9781479913534
SN - 9781479913534
T3 - Proceedings - IEEE Global Communications Conference, GLOBECOM
SP - 3206
EP - 3211
BT - 2013 IEEE Global Communications Conference, GLOBECOM 2013
T2 - 2013 IEEE Global Communications Conference, GLOBECOM 2013
Y2 - 9 December 2013 through 13 December 2013
ER -