On the structural equivalence of two recent algorithms for implicitly trained channel estimation
D.C. McLerno, Aldo Gustavo Orozco-Lugo, M. Lara · 2005
In this paper, two recently published methods for channel estimation based on first-order statistics, are compared. Both arithmetically add a training sequence to the information data, as opposed to the training sequence being placed in a separate empty time-slot, as in say, GSM. However, they each differ in how they process the received data. But the equations for both algorithms, although apparently very different at first glance, are shown in this paper to be completely reducible to identical structures. Finally, as a consequence of this analysis, a new method to implement one of the algorithms is proposed.