Timing error recovery in turbo-coded systems on AWGN channels

Mielczarek, B.; Svensson, Arne

doi:10.1109/tcomm.2002.803976

Cited by 32 publications

(32 citation statements)

References 8 publications

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“…It is shown that the APPA curve after six iterations exhibits a negligible performance degradation with respect to that with ideal synchronisation. Although the BER performance of the conventional NDA method has not been included in this figure, it has been shown in Mielczarek's work [6] that the ML symbol timing recovery algorithm reaches a performance floor in a turbo-coded system and the performance is much worse than their proposed method, which is about 0.1-0.2 dB away from the ideal performance curve.…”

Section: Ber Performancementioning

confidence: 99%

“…Mielczarek et al carried out a programme of research on this issue. Their most recent work [6] proposes a sort of soft bit combination timing recovery approach taking account of the influence of timing offset on turbo decoding. In this approach, a coarse ML NDA detector selects two sets of samples which lie closest to the optimum sampling point from four samples per symbol, and sends to the decoding process.…”

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confidence: 99%

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Iterative APPA Symbol Timing Recovery for Turbo-coded Systems

Zhang¹,

Burr²

2005

Journal of Communication and Information Systems

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Abstract-This paper examines the problem of symbol timing recovery and decoding in turbo-coded systems. The large coding gain of Turbo-codes enables reliable communications at very low signal-to-noise ratio (SNR). However, the application of turbo coding exacerbates the problem of timing recovery due to its very low operating SNRs. Therefore we propose a new concept: 'apriori probability aided' symbol timing recovery, which is suited to coding systems employing iterative soft-in/soft-out (SISO) decoding such as Turbo Codes, where the log-likelihood ratio obtained from a Maximum A Posteriori (MAP) decoder (as used in turbo-decoding) is used to aid estimation in an iterative synchroniser/decoder. This method jointly solves the timing recovery and decoding problem and the synchroniser has effectively no acquisition period. We illustrate this in detail in a turbo-coded BPSK system on AWGN channel. We analyse the performance of the estimator in terms of the mean and the variance of the timing estimate, and demonstrate a substantial performance advantage over conventional non-data-aided (NDA) method. It is also shown that this method can approach the performance of Data-Aided (DA) method without using additional pilot symbols.

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Section: Ber Performancementioning

confidence: 99%

mentioning

confidence: 99%

Iterative APPA Symbol Timing Recovery for Turbo-coded Systems

Zhang¹,

Burr²

2005

Journal of Communication and Information Systems

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“…The authors of [12] have derived semi-analytical expressions of the estimator mean and variance, as function of the timing offset, for a CA decision-directed (DD) timing synchronizer based on MMD. However, the performance evaluation has been made only at low SNR regime, based on the assumption that inter-symbol interference (ISI) could be approximated by an additive Gaussian noise as in [9]. To evaluate one estimator relevance, its MSE is traditionaly compared to lower bounds such as the Cramer-Rao Bounds (CRB) [19] as the one derived for the unknown random phase offset problem in [16], [20].…”

Section: Introductionmentioning

confidence: 99%

Near MAP Dynamical Delay Estimator and Bayesian CRB for Coded QAM Signals

Nasr

Atallah

Cherif

et al. 2018

IEEE Trans. Wireless Commun.

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Abstract-This paper presents an off-line algorithm for dynamical time delay recovery for which the whole observation block is used. The time offset varies over the observation interval following a random walk model. The proposed synchronizer applies to data-aided (DA), non-data-aided (NDA) and code-aided (CA) modes. Theoretical performance of the off-line technique is derived and compared to simulation results. The Bayesian Cramer-Rao Bound (BCRB) is also evaluated for DA, NDA and CA modes and for both the off-line and on-line scenarios. Simulation results show the improvement brought by the off-line and the CA schemes. The presented algorithm outperforms the conventional on-line estimator, which only takes into account the current and previous observations, and its MSE approaches the BCRB.

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“…Different from on-time sample detection, in the absence of synchronization, an optimal Manuscript approach for signals sampled at the symbol rate is taken in [10], [11]; additionally, [12] presents a further analysis that handles arbitrary baseband pulse shapes. Another approach can be found in [13]: based on a coarse timing estimation, the sequence estimation is performed by combining soft bits that are obtained by the turbo decoder. A ML estimator for time delay estimation from DS-CDMA multipath transmissions is proposed in [14], in which the EM algorithm is applied to decouple the computation task into a parallel processor implementation.…”

Section: Introductionmentioning

confidence: 99%

ML-Based Iterative Sequence Estimation Without Symbol Timing Recovery

Shen

et al. 2013

IEEE Commun. Lett.

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This paper presents a novel iterative scheme of maximum-likelihood (ML) sequence estimation in the absence of timing information. The ML estimates of the symbols are obtained from the oversampling samples of the matched filter output without timing estimation or sample interpolation, eliminating the need for a separate synchronizer. With unsynchronized samples, the detection problem is treated as ML estimation from incomplete data, and the expectation-maximization algorithm is applied to find an iterative solution. The proposed scheme is compared with conventional techniques under different modulation orders. The simulation results indicate that acceptable bit error rate performance can be achieved for short package transmission.

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Timing error recovery in turbo-coded systems on AWGN channels

Cited by 32 publications

References 8 publications

Iterative APPA Symbol Timing Recovery for Turbo-coded Systems

Iterative APPA Symbol Timing Recovery for Turbo-coded Systems

Near MAP Dynamical Delay Estimator and Bayesian CRB for Coded QAM Signals

ML-Based Iterative Sequence Estimation Without Symbol Timing Recovery

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