Data-aided frequency offset estimation in frequency selective channels: Training sequence selection

Abstract

We consider the problem of frequency-offset estimation in frequency selective channels in a data-aided context. More specifically, we address the training sequence selection issue with the goal of providing the most accurate frequency offset estimates. Towards this end, we examine the Cramer-Rao bound (CRB) for the problem at hand. Since it is hardly feasible to derive the training sequence that results in a minimum CRB, an expression for the asymptotic CRB is derived which depends in a simple way on the channel impulse response and the training sequence correlation. Based on the asymptotic CRB, two methods are presented to select an optimal training sequence. Numerical simulations illustrate the estimation performance obtained with these training sequences.