Comparison between asymmetric generalized Gaussian (AGG) and symmetric-\u3b1-stable (S\u3b1S) noise models for signal estimation in non Gaussian environments

The paper is focused on the problem of multilevel digital signal estimation in the presence of generic noise in a communication system. Noise is assumed unimodal, independent identically distributed, generically non Gaussian, that is eventually asymmetric, impulsive or not. The proposed solution is based on a previously developed estimator which requires the analytical probability density function model of the noise. The selected estimator was originally applied under the assumption of S\u3b1S noise distribution. In this paper, the asymmetric generalized Gaussian (agg) model is selected as a suitable model to describe the noise processes: hence, it is discussed and compared with the S\u3b1S distributions in terms of decoding performances. Tests were performed on simulated binary sequences corrupted by interference generated as S\u3b1S processes. Test results outlines comparable performances of the two families of parametric noise models