Paper Info
Title
Cognitive radio spectrum prediction using dictionary learning
Abstract
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.
Year
DOI
Venue
2013
10.1109/GLOCOM.2013.6831565
GLOBECOM
Keywords
Field
DocType
primary transmitters,cr node locations,compressive sensing,cognitive radio,dictionary learning,cognitive radio spectrum prediction,compressed sensing,spatio-temporal spectrum prediction algorithms
Numerical tests,Online algorithm,Dictionary learning,Computer science,A priori and a posteriori,Prediction algorithms,Artificial intelligence,Interference (wave propagation),Machine learning,Compressed sensing,Cognitive radio
Conference
ISSN
Citations 
PageRank 
2334-0983
6
0.50
References 
Authors
10
2
Name
Order
Citations
PageRank
Seung-Jun Kim1100362.52
Georgios B. Giannakis24977340.58