An asymptotic LMPI test for cyclostationarity detection with application to cognitive radio
David Ramírez, Peter J. Schreier, Javier Vía, Ignacio Santamaría, Louis L. Scharf
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.
BibTeX
@inproceedings{icassp2015_anasymptoticlmpi,
title = {An asymptotic LMPI test for cyclostationarity detection with application to cognitive radio},
author = {David Ramírez and Peter J. Schreier and Javier Vía and Ignacio Santamaría and Louis L. Scharf},
booktitle = {ICASSP 2015},
year = {2015}
}