Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Bayesian and hybrid Cramer-Rao bounds for QAM dynamical phase estimation
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Signal Processing
Performance Analysis of ML-Based Feedback Carrier Phase Synchronizers for Coded Signals
IEEE Transactions on Signal Processing
Analytic and Asymptotic Analysis of Bayesian CramÉr–Rao Bound for Dynamical Phase Offset Estimation
IEEE Transactions on Signal Processing
Iterative carrier phase recovery suited to turbo-coded systems
IEEE Transactions on Wireless Communications
Algorithms for iterative decoding in the presence of strong phase noise
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Signal Processing
Approximate expressions for Cramer-Rao bounds of code aided QAM dynamical phase estimation
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
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This paper presents a near-optimum, low-complexity, fixed-interval smoothing algorithm that approaches the performance of an optimal smoother for the price of two low-complexity sequential estimators (two PLLs). The proposed Smoothing PLL (S-PLL) algorithm is easy to implement and fits the Cramer-Rao bounds over a wide range of signal-to-noise ratios. Moreover we show that, compared to the conventional forward loop, the proposed scheme allows to have a large gain of several dBs and is able to track frequency offsets.