Model selection for adaptive robust parameter estimation and its impact on multiuser detection

Robust parameter estimation in impulsive noise environments has become an important issue in wireless communications. In previous work, an adaptive robust estimator was developed which modelled the noise score function as a weighted sum of basis functions where the weights best fitted the empirical distribution. Here, this adaptive robust estimator is extended by using model selection to find a parsimonious set of basis functions to model the unknown noise distribution thereby improving small sample performance. It was found that the best model for small sample sizes is a single basis. Finally, we apply this procedure to robust multiuser detection in impulsive noise channels.