Paper Info
Title
An Asymptotic Lmpi Test For Cyclostationarity Detection With Application To Cognitive Radio
Abstract
We propose a new detector of primary users in cognitive radio networks. The main novelty of the proposed detector in comparison to most known detectors is that it is based on sound statistical principles for detecting cyclostationary signals. In particular, the proposed detector is (asymptotically) the locally most powerful invariant test, i.e. the best invariant detector for low signal-to-noise ratios. The derivation is based on two main ideas: the relationship between a scalar-valued cyclostationary signal and a vector-valued wide-sense stationary signal, and Wijsman's theorem. Moreover, using the spectral representation for the cyclostationary time series, the detector has an insightful interpretation, and implementation, as the broadband coherence between frequencies that are separated by multiples of the cycle frequency. Finally, simulations confirm that the proposed detector performs better than previous approaches.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP)
Cyclostationarity, Hypothesis test, Maximal invariant, Locally most powerful invariant test (LMPIT), Toeplitz matrices
Field
DocType
ISSN
Pattern recognition,Computer science,Signal-to-noise ratio,Stationary process,Coherence (physics),Invariant (mathematics),Artificial intelligence,Detector,Statistical hypothesis testing,Cyclostationary process,Cognitive radio
Conference
1520-6149
Citations 
PageRank 
References 
0
0.34
14
Authors
5
Name
Order
Citations
PageRank
David Ramírez120620.05
Peter J. Schreier2104.26
Javier Vía341240.39
Ignacio Santamaria4236.62
Louis L. Scharf52525414.45