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. Due to the increasing requirements of modern communication systems to face the physical channel (low signal-to-noise ratio, high data rates), phase estimation is more challenging than ever before. Since phase errors rapidly degrade the overall performance of communication systems, synchronization has recently become one of the most challenging tasks that a digital receiver has to cope with. Noels et al [1],[2]X derived a maximum likelihood (ML) algorithm for the problem of constant phase estimation, and then applied a first-order and a second order phase-locked loop (PLL) based algorithm for the coded BPSK and QPSK dynamical phase estimation. The corresponding performances are limited both by the on-line bound and by a non-zero phase MSE floor. On the contrary, this paper deals with the non data aided (NDA) estimation of a time-varying phase and proposes an off-line Smoothing PLL (S-PLL) algorithm. To assess the performance of such algorithms, Bayesian and hybrid Cramér-Rao Bounds (BCRB and HCRB) associated to this dynamical phase synchronization problem have already been considered in some recent contributions X[3]XX-[6]X and clearly show the superiority of the off-line scenario [7].