Efficient carrier frequency offset estimation for OFDM using periodic training sequences

Abstract

Periodic training sequences for carrier frequency offset estimation in orthogonal frequency division multiplexing (OFDM) systems are discussed. By exploiting the independent conditional Probability Density Functions (PDF) of different subblocks in a received training sequence, a new complexity efficient frequency offset estimator is proposed in this paper. The same accuracy as that of Best Linear Unbiased Estimator (BLUE) proposed by Morelli can be achieved in the proposed algorithm, however, with a complexity of only about 4/(3M-2) that of the later (M is the number of sub-blocks that a training sequence comprising). A new frequency offset acquisition algorithm is also proposed in this paper, whose maximum acquisition range is up to ±M/2 times subcarrier spacing, and a negligible acquisition error probability can be achieved. Since the complexity of the proposed algorithm doesn't change as the increases of M (as compared to it, the complexity of Morelli algorithm is a monotonously increasing function of M), its estimation accuracy can be improved by optimizing M without degrading its complexity efficiency, as proven by computer simulation.