Enhanced time-varying channel estimation based on two dimensional basis projection and self-interference suppression

Abstract

Estimation of single-carrier communication channels based on superimposed training (ST) has been widely studied in the last years, because it offers a way for estimating the channel without the transmission of a pilot sequence multiplexed in time with the data, which implies a saving of valuable bandwidth. In this paper, the use of a variation of ST known as data-dependent superimposed training (DDST) in conjunction with a bi-dimensional basis expansion is proposed in order to enhance the performance of the channel estimation. For corroboration of the exposed method, simulation results are presented, where it is possible to observe a favorable performance with respect to state of art techniques.