An iterative method for improved training-based estimation of doubly selective channels

A new approach has recently been proposed to describe doubly selective channels (i.e., time- and frequency-selective channels) with a limited number of parameters; it is referred to as the basis expansion model (BEM). In the BEM, the true channel coefficients are approximated with a high accuracy using a limited number of complex exponentials. We propose a new method in order to identify the BEM coefficients of the transmission channel. We consider a transmission scheme where several short training sequences (i.e. their length is comparable to the channel order) are inserted in the stream of data symbols. We propose an iterative method that exploits all the received symbols that contain contributions from the training sequences and blindly filters out the contribution of the unknown surrounding data symbols. The proposed method has a low computational complexity and outperforms existing methods proposed in a similar context.