Composite Frequency-Offset Estimator for Wireless Communications

The paper deals with the problem of the carrier frequency-offset (CFO) estimation for wireless communications. Since the well-known maximum-likelihood (ML) CFO estimator is rather complex, many complexity-reduced CFO estimators have so far been proposed. However, most of them are based on periodic training sequences and have limited estimation range. We propose a general framework called composite frequency- offset estimator (CFE), which can extend the estimation range of any range-limited correlation-based CFO estimator up to the full transmission spectrum. We first formulate the CFO estimation as a problem of composite hypothesis testing, and then solve the problem by the composite hypothesis testing approaches. We show that the composite frequency-offset estimate is the ML estimate as long as the original range-limited estimator is unbiased and attains the Cramer-Rao Lower Bound. The CFE also allows a flexible tradeoff between the estimation range and computational complexity. Finally, numerical results show the efficiency of the proposed method.