A stochastic method for training based channel identification

In this paper, we propose a new iterative stochastic method to identify convolutive channels when training sequences are inserted in the transmitted signal. We consider the case where the channel is quasistatic (i.e. the sampling period is several orders of magnitude below the coherence time of the channel). There are no requirements on the length of the training sequences and all the received symbols that contain contributions from the training symbols are exploited. The interference from the unknown data symbols surrounding the training sequences is considered as additive noise colored by the transmission channel. An iterative weighted least squares approach is used to filter out the contribution of both this interference term and the additive white gaussian noise term.