An Optimal Training Signal Structure for Frequency-Offset Estimation

Abstract

This paper addresses an optimal training-signal design for frequency-offset estimation. Based on minimizing the Cramer-Rao lower bound for frequency-offset estimation with constraints on the peak and the total training signal energies, and the training block length, the optimal training-signal structure is developed. An approximate version of the optimal training-signal structure is proposed, which has practically the same performance as the optimal one, and provides convenience in training-signal generation and estimator derivation. Two robust reduced-complexity frequency-offset estimation methods for the proposed training structures are presented. In order to handle larger frequency offsets, modified training-signal structures are proposed. Frequency-offset estimation methods suitable for these training signals are also derived, based on the best linear unbiased estimation principle. Analytical and simulation results show that the proposed training-signal structures improve the estimation performance significantly.