Improved decoding with the SOVA in a parallel concatenated (Turbo-code) scheme

Papke, L.; Robertson, Patrick; Villebrun, E.

doi:10.1109/icc.1996.540253

Cited by 98 publications

(35 citation statements)

References 5 publications

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“…Based on the remarks in [41], it is logical to define the extrinsic information as the difference between the generated soft-output value and the coherent term present in , related to the same information symbol, according to (23) We now comment on the role of switch S in Fig. 2.…”

Section: A Parallel Concatenated Codesmentioning

confidence: 99%

Noncoherent iterative (turbo) decoding

Colavolpe

Ferrari²,

Raheli³

2000

IEEE Trans. Commun.

100

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Abstract-Recently, noncoherent sequence detection schemes for coded linear and continuous phase modulations have been proposed, which deliver hard decisions by means of a Viterbi algorithm. The current trend in digital transmission systems toward iterative decoding algorithms motivates an extension of these schemes. In this paper, we propose two noncoherent soft-output decoding algorithms. The first solution has a structure similar to that of the well-known algorithm by Bahl et al.(BCJR), whereas the second is based on noncoherent sequence detection and a reduced-state soft-output Viterbi algorithm.Applications to the combined detection and decoding of differential or convolutional codes are considered. Further applications to noncoherent iterative decoding of turbo codes and serially concatenated interleaved codes are also considered. The proposed noncoherent detection schemes exhibit moderate performance loss with respect to corresponding coherent schemes and are very robust to phase and frequency instabilities.Index Terms-Iterative decoding, noncoherent decoding/detection, soft-input/soft-output algorithms, turbo (de)coding.

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Section: A Parallel Concatenated Codesmentioning

confidence: 99%

Noncoherent iterative (turbo) decoding

Colavolpe

Ferrari²,

Raheli³

2000

IEEE Trans. Commun.

100

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“…This represents a highly wasteful system and would not be practical. It has been suggested that the high correlation between the a priori information and the extrinsic information of a module even in a turbo system can cause the performance degradation [22], [23]. In this section, we show a proper way to construct the a priori information for the main equalizer based on the extrinsic information generated by the branch equalizers when their outputs are correlated with the main equalizer output.…”

Section: Self-iterating Soft Equalizer Algorithmmentioning

confidence: 99%

Self-Iterating Soft Equalizer

Jeong

Moon

2013

IEEE Trans. Commun.

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A self-iterating soft equalizer (SISE) consisting of a few relatively weak constituent equalizers is shown to provide robust performance even in severe intersymbol interference (ISI) channels that exhibit deep nulls and valleys within the signal band. Constituent equalizers are allowed to exchange soft information in the absence of interleavers based on the method that are designed to suppress significant correlation among their soft outputs. The resulting SISE works well as a stand-alone equalizer or as the equalizer component of a turbo equalization system. The performance advantages over existing methods are validated with bit-error-rate (BER) simulations and extrinsic information transfer (EXIT) chart analysis. It is shown that in turbo equalizer setting the SISE achieves performance closer to the maximum a posteriori probability equalizer than any other known schemes in very severe ISI channels.

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“…It has been proposed to scale the extrinsic information exchanged between the constituent decoders [7][8][9]. With this modification equation (11) for branch metric calculations can be rewritten as…”

Section: Extrinsic Information Scalingmentioning

confidence: 99%

“…Extrinsic information scaling has been proposed to compensate for the optimistic LLR calculations of SOVA [9]. A gain of 0.4 dB has been reported for a code of memory length 4 at BER of 10 -4 [9]. Scaling factor modification has also been applied and tested on the MaxLog-MAP algorithm.…”

Section: Extrinsic Information Scalingmentioning

confidence: 99%

A comparative study on the modified Max-Log-MAP turbo decoding by extrinsic information scaling

Taskaldiran

Morling

Kale

2007

2007 Wireless Telecommunications Symposium

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A simple but effective technique to improve the performance of the Max-Log-MAP algorithm is to scale the extrinsic information exchanged between two MAP decoders. A comprehensive analysis of the selection of the scaling factors according to channel conditions and decoding iterations is presented in this paper. Choosing a constant scaling factor for all SNRs and iterations is compared with the best scaling factor selection for changing channel conditions and decoding iterations. It is observed that a constant scaling factor for all channel conditions and decoding iterations is the best solution and provides a 0.2-0.4 dB gain over the standard MaxLog-MAP algorithm. Therefore, a constant scaling factor should be chosen for the best compromise.

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Improved decoding with the SOVA in a parallel concatenated (Turbo-code) scheme

Cited by 98 publications

References 5 publications

Noncoherent iterative (turbo) decoding

Noncoherent iterative (turbo) decoding

Self-Iterating Soft Equalizer

A comparative study on the modified Max-Log-MAP turbo decoding by extrinsic information scaling

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