An Iterative ML-based Carrier Frequency Estimation Algorithm

We propose an iterative data-aided algorithm based on maximum likelihood criteria for carrier frequency estimation in burst-mode phase shift keying (PSK) transmission. The proposed algorithm has a low threshold and its estimation range is large, about plusmn40% of the symbol rate. In addition, its accuracy is close to the Cramer-Rao bound (CRB) at signal-to-noise ratio (SNR) above threshold. The performance of the proposed algorithm is better and its computational complexity is also lower compared with previous ML-based algorithms.