PROJECT TITLE :

Cramér-Rao Bounds for SNR Estimation of Oversampled Linearly Modulated Signals

ABSTRACT:

Most signal-to-noise ratio (SNR) estimators use the receiver matched filter output sampled at the symbol rate, an approach which will not preserve all data in the analog waveform due to aliasing. Therefore, it is relevant to ask whether or not avoiding aliasing may improve SNR estimation. To this end, we tend to compute the corresponding data-aided (DA) and nondata-aided (NDA) Cramér-Rao bounds (CRBs). We have a tendency to adopt a novel twin filter framework, which is shown to be information-preserving under appropriate conditions and considerably simplifies the analysis. It is shown that the CRB will be substantially reduced by exploiting any on the market excess bandwidth, depending on the modulation theme, the SNR range, and also the estimator type (DA or NDA).