@article {769, title = {Cram{\'e}r-Rao Bounds for SNR Estimation of Oversampled Linearly Modulated Signals}, journal = {IEEE Trans. Signal Processing}, volume = {63}, year = {2015}, month = {04/2015}, pages = {1675-1683}, chapter = {1675}, abstract = {
Most Signal-to-noise ratio (SNR) estimators use the
receiver matched filter output sampled at the symbol rate, an
approach which does not preserve all information in the analog
waveform due to aliasing. Thus, it is relevant to ask whether
avoiding aliasing could improve SNR estimation. To this end, we
compute the corresponding data-aided (DA) and non-data-aided
(NDA) Cram{\'e}r-Rao bounds (CRBs). We adopt a novel dual filter
framework, which is shown to be information-preserving under
suitable conditions and considerably simplifies the analysis. It is
shown that the CRB can be substantially reduced by exploiting
any available excess bandwidth, depending on the modulation
scheme, the SNR range, and the estimator (DA or NDA) type.
}, keywords = {adaptive signal processing, compass, Cram{\'e}r-Rao bound (CRB), dynacs, oversampling, Signal-to-noise ratio (SNR)}, doi = {10.1109/TSP.2015.2396013}, author = {R. L{\'o}pez-Valcarce and J. Villares and J. Riba and W. Gappmair and C. Mosquera} }