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In digital communication receivers, ambiguities in terms of timing and phase need to be resolved prior to data detection. In the presence of powerful error-correcting codes, which operate in low signal-to-noise ratios (SNR), long training sequences are needed to achieve good performance. In this contribution, we develop a new class of code-aided ambiguity resolution algorithms, which require no training sequence and achieve good performance with reasonable complexity. In particular, we focus on algorithms that compute the maximum-likelihood (ML) solution (exactly or in good approximation) with a tractable complexity, using a factor-graph representation. The complexity of the proposed algorithm is discussed and reduced complexity variations, including stopping criteria and sequential implementation, are developed. [ABSTRACT FROM AUTHOR]

In this paper, we present a closed-form expression of a Bayesian Cramer-Ran lower bound for the estimation of a dynamical phase offset in a non-data-aided BPSK transmitting context. This kind of bound is derived considering two different scenarios: a first expression is obtained in an offline context, and then a second expression in an online context logically follows. The SNR-asymptotic expressions of this bound drive us to introduce a new asymptotic bound, namely the asymptotic Bayesian Cramér-Rao Bound. This bound is close to the classical Bayesian bound but is easier to evaluate. [ABSTRACT FROM AUTHOR]

In this letter, we address the issue of symbol timing recovery for a coded burst transmission system. As direct maximum-likelihood (ML) estimation is intractable, we resort to the expectation-maximization (EM) algorithm in order to derive a receiver that iterates between data detection and synchronization. Conventional data-aided (DA) and decision-directed (DD) synchronizers can be interpreted as special cases of the proposed algorithm. The EM-based technique takes into account code proper- ties and is especially well suited to scenarios where conventional schemes fail to provide the detector with a reliable timing estimate. The performance of the proposed algorithm is compared with conventional techniques through computer simulations, both in terms of mean-square estimation error (MSEE) and bit error rate (BER). [ABSTRACT FROM AUTHOR]

The introduction of turbo and low-density paritycheck (LDPC) codes with iterative decoding that almost attain Shannon capacity challenges the synchronization subsystems of a data modem. Fast and accurate signal synchronization has to be performed at a much lower value of signal-to-noise ratio (SNR) than in previous less efficiently coded systems. The solution to this issue is developing specific synchronization techniques that take advantage of the presence of the channel code and of the iterative nature of decoding: the so-called turbo-synchronization algorithms: The aim of this paper within this special issue devoted to the turbo principle is twofold: on the one hand, it shows how the many turbo-synchronization algorithms that have already appeared in the literature can be cast into a simple and rigorous theoretical framework. On the other hand, it shows the application of such techniques in a few simple cases, and evaluates improvement that can be obtained from them, especially in the low-SNR regime. [ABSTRACT FROM AUTHOR]

This paper deals with maximum-likelihood (ML) estimation of synchronization parameters for coded transmission systems. In particular, we present a unified framework based on both the sum-product (SP) algorithm and the expectation-maximization (EM) algorithm for the design of iterative synchronizers. The proposed approach is shown to encompass some known iterative synchronizers. In particular, we revisit a previously proposed framework based on the EM algorithm only by means of our ‘SP-EM’ approach. The performance of the proposed synchronization method is assessed in terms of mean-square error and bit-error rate by simulation results. In particular, we consider the joint synchronization of the timing epoch and the carrier phase offset in the case of convolutionally coded and turbo-coded transmissions. [ABSTRACT FROM AUTHOR]