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A data-aided frequency offset estimation algorithm suited for burst-mode communications is described and analyzed. The proposed algorithm is based on a Pilot symbol assisted modulation (PSAM) frame structure, which consists of regularly spaced pilot symbols and a group of consecutive pilot symbols embedded in. Firstly, an initial estimate based on consecutive pilot symbols is generated for reducing the phase ambiguity. Secondly, based on both spaced and consecutive pilot symbols, fine preliminary estimates are generated to improve estimation accuracy. Lastly, the final estimate is obtained by combining the fine preliminary estimates with the initial. This three-step algorithm is shown to provide high estimation accuracy close to those of FFT-based algorithm at low Signal-to-noise ratio (SNR). In addition, it is suitable for high carrier frequency offset cases.

An iterative data-aided algorithm based on maximum likelihood (ML) for carrier frequency estimation under low signal-to-noise ratio (SNR) environment is proposed. Simulation results show that it can achieve about 3 dB lower SNR threshold when compared with M and M algorithm. Its estimation range is large, about 40% of the symbol rate and its accuracy is very close to FFT-based maximum likelihood frequency estimation algorithm and the Cramer-Rao lower bound (CRLB). Moreover, the simplified estimator based on proposed algorithm has both lower threshold and less computational complexity when compared with iterative linear prediction (ILP) algorithm.