ML and EM algorithm for non-data-aided SNR estimation of linearly modulated signals

@article{Gappmair2008MLAE,
  title={ML and EM algorithm for non-data-aided SNR estimation of linearly modulated signals},
  author={Wilfried Gappmair and Roberto L{\'o}pez-Valcarce and Carlos Mosquera},
  journal={2008 6th International Symposium on Communication Systems, Networks and Digital Signal Processing},
  year={2008},
  pages={530-534}
}
The recently published Cramer-Rao lower bound for non-data-aided (NDA) estimation of the signal-to-noise ratio (SNR) reveals a considerable gap, when compared to the jitter performance of NDA algorithms available from the open literature. The maximum-likelihood (ML) solution derived in this paper closes this gap. However, the latter provides a set of two nonlinear vector equations, which might be simplified only for modulation schemes with constant envelope like M-ary PSK. For signals with… CONTINUE READING
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