A Low-Complexity ML Estimator for Carrier and Sampling Frequency Offsets in OFDM Systems

Abstract

This letter considers the joint acquisition of the carrier frequency offset (CFO) and sampling frequency offset (SFO) in OFDM systems using two long training symbols in the preamble. Conventional maximum-likelihood (ML) methods require a two-dimensional exhaustive search. To overcome this problem, a low-complexity closed-form ML estimator is proposed. It is shown that the CFO can be solved in closed-form. Then we develop an approximate ML estimation algorithm for the SFO by taking the second-order Taylor series expansion. Simulation results show that the proposed algorithm achieves almost the same performance as existing ML methods, but no exhaustive search is needed.