Turbo decoding for PR4: parallel versus serial concatenation

Souvignier, T.; Friedmann, A.; Oberg, M.; Siegel, Paul H.; Swanson, R.E.; Wolf, J.K.

doi:10.1109/icc.1999.765510

Cited by 87 publications

(58 citation statements)

References 7 publications

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“…The step that is needed to close the loop of a single density evolution round is the evolution of the average density into the average density , i.e., the evolution of message densities through the trellis portion of the joint code/channel graph. We denote this step as (45) where is symbolic notation for trellis evolution and denotes the pdf of the channel noise (in this case a zero-mean Gaussian with variance ). Even though no closed-form solution for (45) is known, it can be calculated numerically using Monte Carlo techniques.…”

Section: A Density Evolutionmentioning

confidence: 99%

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Binary intersymbol interference channels: gallager codes, density evolution, and code performance bounds

Kavcic

Mitzenmacher

2003

IEEE Trans. Inform. Theory

169

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Abstract-We study the limits of performance of Gallager codes (low-density parity-check (LDPC) codes) over binary linear intersymbol interference (ISI) channels with additive white Gaussian noise (AWGN). Using the graph representations of the channel, the code, and the sum-product message-passing detector/decoder, we prove two error concentration theorems. Our proofs expand on previous work by handling complications introduced by the channel memory. We circumvent these problems by considering not just linear Gallager codes but also their cosets and by distinguishing between different types of message flow neighborhoods depending on the actual transmitted symbols. We compute the noise tolerance threshold using a suitably developed density evolution algorithm and verify, by simulation, that the thresholds represent accurate predictions of the performance of the iterative sum-product algorithm for finite (but large) block lengths. We also demonstrate that for high rates, the thresholds are very close to the theoretical limit of performance for Gallager codes over ISI channels. If C denotes the capacity of a binary ISI channel and if C i i d denotes the maximal achievable mutual information rate when the channel inputs are independent and identically distributed (i.i.d.) binary random variables (C i i d C), we prove that the maximum information rate achievable by the sum-product decoder of a Gallager (coset) code is upper-bounded by C i i d . The last topic investigated is the performance limit of the decoder if the trellis portion of the sum-product algorithm is executed only once; this demonstrates the potential for trading off the computational requirements and the performance of the decoder.Index Terms-Bahl-Cocke-Jelinek-Raviv (BCJR)-once bound, channel capacity, density evolution, Gallager codes, independent and identically distributed (i.i.d.) capacity, intersymbol interference (ISI) channel, low-density parity-check (LDPC) codes, sumproduct algorithm, turbo equalization.

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Section: A Density Evolutionmentioning

confidence: 99%

“…Due to the high computational complexity of the BCJR algorihm, several authors suggest applying the BCJR step only once [33], [45] and subsequently iterating the message-passing decoding algorithm only within the code subgraph of the joint channel/code graph (see Fig. 3).…”

Section: The Bcjr-once Boundmentioning

confidence: 99%

Binary intersymbol interference channels: gallager codes, density evolution, and code performance bounds

Kavcic

Mitzenmacher

2003

IEEE Trans. Inform. Theory

169

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“…In the conventional receiver, conventional timing recovery is followed by a turbo equalizer [11] (see Fig. 1), which iteratively exchanges soft information between the SISO detector and the SISO decoder.…”

Section: Channel Descriptionmentioning

confidence: 99%

Robustness of per-survivor iterative timing recovery in perpendicular recording channels

Kovintavewat

Barry

Erden

et al. 2005

INTERMAG Asia 2005. Digests of the IEEE International Magnetics Conference, 2005.

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Per-survivor iterative timing recovery was proposed by Kovintavewat, Barry, Erden, and Kurtas [1] to perform timing recovery, equalization, and errorcorrection decoding jointly. In this paper, we investigate the robustness of this scheme in the situation where the channel is dominated by media jitter noise. We apply the pattern-dependent noisepredictive (PDNP) technique to per-survivor iterative timing recovery. Results indicate that the persurvivor iterative timing recovery with the PDNP technique is more robust than other iterative timing recovery schemes when operating in media noise dominated channel.

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“…In the conventional receiver, conventional timing recovery is followed by a turbo equalizer [3] (see Fig. 2), which iteratively exchanges soft information between the SISO equalizer for the precoded PR-IV channel and the SISO decoder.…”

Section: Channel Descriptionmentioning

confidence: 99%

“…Fortunately, a solution to this problem with complexity comparable to the conventional receiver has been proposed by Nayak, Barry, and McLaughlin [1], which will be referred to as the NBM scheme. It is realized by embedding the timing recovery step inside the turbo equalizer [3] so as to perform those three tasks jointly. Nonetheless, this scheme requires a large number of turbo iterations to provide a good performance even with a cycle slip [4] detection and correction algorithm as used in [1], especially when the timing jitter is large.…”

Section: Introductionmentioning

confidence: 99%

Per-survivor iterative timing recovery for coded partial response channels

Kovintavewat

Barry

Erden³

et al.

IEEE Global Telecommunications Conference, 2004. GLOBECOM '04.

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Abstract-We propose a new iterative timing recovery scheme based on per-survivor processing that jointly performs timing recovery and turbo equalization on partial response channels with error-correction codes. The scheme embeds the timing recovery process inside the Bahl, Cocke, Jelinek, and Raviv (BCJR) equalizer using per-survivor processing. This per-survivor BCJR equalizer then iteratively exchanges soft information with an error-correction decoder. Results indicate that the proposed scheme yields a better performance than a conventional receiver that performs timing recovery and turbo equalization separately, and also the iterative timing recovery scheme proposed in [1], especially when the channel encounters severe timing jitter noise. We also present evidence that suggests that the proposed scheme can correct a cycle slip much more efficiently than the others.

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Turbo decoding for PR4: parallel versus serial concatenation

Cited by 87 publications

References 7 publications

Binary intersymbol interference channels: gallager codes, density evolution, and code performance bounds

Binary intersymbol interference channels: gallager codes, density evolution, and code performance bounds

Robustness of per-survivor iterative timing recovery in perpendicular recording channels

Per-survivor iterative timing recovery for coded partial response channels

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