ML-based estimator for integer frequency offset estimation of OFDM systems

A novel estimator for integer frequency offset estimation of OFDM systems is derived, which is based on the maximum likelihood (ML) technique and exploits the differential information between two consecutive blocks of OFDM data symbols in the frequency domain. The reason why the ML estimator has better performance than the conventional method is analyzed. How to select the differential sequence is also studied. By computer simulations, the performance of the ML estimator is compared with that of the conventional method for the additive white Gaussian noise (AWGN) channel and the multipath fading channel. The simulation results are in good agreement with the analytical study.