A System on a Programmable Chip Architecture for Data-Dependent Superimposed Training Channel Estimation

Abstract

Channel estimation in wireless communication systems is usually accomplished by inserting, along with the information, a series of known symbols, whose analysis is used to define the parameters of the filters that remove the distortion of the data. Nevertheless, a part of the available bandwidth has to be destined to these symbols. Until now, no alternative solution has demonstrated to be fully satisfying for commercial use, but one technique that looks promising is superimposed training (ST). This work describes a hybrid software-hardware FPGA implementation of a recent algorithm that belongs to the ST family, known as Data-dependent Superimposed Training (DDST), which does not need extra bandwidth for its training sequences (TS) as it adds them arithmetically to the data. DDST also adds a third sequence known as data-dependent sequence, that destroys the interference caused by the data over the TS. As DDST's computational burden is too high for the commercial processors used in mobile systems, a System on a Programmable Chip (SOPC) approach is used in order to solve the problem.