A Precoding Scheme for Eliminating Data Identification Problem in Single Carrier System Using Data-Dependent Superimposed Training

In single carrier (SC) systems using a data-dependent superimposed training (DDST) scheme, the data-induced interference that occurs during channel estimation is eliminated, but at the expense of data distortion. This distortion causes a data identification problem (DIP), in which the data sequence cannot be uniquely identified at the receiver end. Accordingly, the present study proposes a precoding scheme which solves the DIP in SC systems using DDST. Five design criteria are derived for the precoding matrix in order to eliminate DIP, achieve frequency diversity, maintain a low peak-to-average power ratio (PAPR), and minimize the receiver complexity. In addition, a low-complexity detection method is proposed for reducing the search space of the detection process. The simulation results show that the proposed precoding scheme successfully eliminates the DIP in traditional DDST schemes while maintaining the same PAPR as that of the traditional SC systems based on DDST.