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) Cramér-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.

%B IEEE Trans. Signal Processing
%V 63
%P 1675-1683
%8 04/2015
%G eng
%N 7
%& 1675
%R 10.1109/TSP.2015.2396013