A Novel ML-Based Frequency Offset Estimator for OFDM Systems

A novel carrier frequency offset (CFO) estimation algorithm based on the maximum-likelihood (ML) criterion for orthogonal frequency division multiplexing (OFDM) systems is proposed. In the proposed method, a well-known preamble with excellent timing synchronization is modified, and based on the improved preamble three fractional frequency offset estimators are investigated. The estimation range and the mean-square-error (MSE) performance under various signal-to-noise ratios (SNR) of the estimators are discussed. Finally, our proposed estimators are compared with the traditional Schmidl (SC) algorithm in terms of MSE. Simulation results demonstrate that the proposed methods have better performance than the SC frequency offset estimation algorithm in AWGN and multipath fading channels without increasing complexity. Moreover, the special characteristics of the improved preamble lead to a superior timing performance.