Lowering the Error Floor of Turbo Codes With CRC Verification

Tonnellier, Thibaud; Leroux, Camille; Gal, Bertrand Le; Gadat, Benjamin; Jégo, Christophe; Wambeke, Nicolas Van

doi:10.1109/lwc.2016.2571283

Cited by 27 publications

(13 citation statements)

References 14 publications

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“…When m ≤ n/2, if only one vector is processed, most of the PE's stay in the idle mode. EBS offers the possibility to take profit of the idling PE to either process several independent frames in parallel, and thus increase the processing throughput, or to process the same frame with several decoding hypothesis to increase the decoding performance using techniques likes [8] or [9] to name only a few. In both cases a 'Single Instruction, Multiple Data (SIMD)' type of processing should be used, i.e., vectors of same size should be concurrently rotated by the same index.…”

Section: B Parallelismmentioning

confidence: 99%

Extended Barrel-Shifter for Versatile QC-LDPC Decoders

Boutillon

Harb

2020

IEEE Wireless Commun. Lett.

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In this paper, we present a Barrel-Shifter of size n extended by an additional layer that can handle any circular permutation on a vector of size m, m ≤ n, thanks to a specific initial positioning of the data. The construction of the so-called Extended Barrel-Shifter is motivated by the hardware decoder constraint related to the Low-Density Parity-Check (LDPC) code recently adopted for the 5G mobile standard. The proposed algorithm requires 42 % less multiplexers than the best stateof-the-art solution for n = 384 of the 5G LDPC standard. This proposal is also able to process several inputs in parallel without extra hardware cost.

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Section: B Parallelismmentioning

confidence: 99%

Extended Barrel-Shifter for Versatile QC-LDPC Decoders

Boutillon

Harb

2020

IEEE Wireless Commun. Lett.

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“…The low free distance results in the BER curve flattening after entering the "error floor" region, making it extremely difficult to achieve a very low probability of error even if the signal-to-noise ratio continuously grows. Recent studies [103] showed that the aforementioned problem can be mitigated by adopting both interleave design improvements and cyclic redundancy check codes.…”

Section: Turbo Codesmentioning

confidence: 99%

Network for hypersonic UCAV swarms

Luo¹,

Zhang²,

Wang³

et al. 2020

Sci. China Inf. Sci.

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Unmanned combat aerial vehicles (UCAVs) that swarm with both autonomous decision-making and cooperative attacking have been regarded as revolutionary elements of modern warfare. In such a swarm, inter-group connectivity must be ensured in a network to maintain a collective consensus. In recent years, academia and industry have made many efforts to achieve common tactical data link systems and commercial drone networks. However, the existing results have difficulty meeting the needs of cooperative autonomous UCAV swarms with both hypersonic mobility and time sensitivity in severe confrontation scenarios. In this article, we conduct an in-depth investigation of the network used for the hypersonic UCAV swarms, which can be considered as a special form of mobile wireless network. Furthermore, faced with specific functional demands, we summarize the main challenges of designing this dedicated network. In addition, a comprehensive survey of potential solutions for the network design is presented. Lastly, we discuss the possible capabilities of the network given the current forefront of technology, as well as remaining challenges and open issues.

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“…Concatenating another outer code such as the Bose-Chaudhuri-Hocquenghem (BCH) code is also widely used [18]. Applying additional check code such as cyclic redundancy check (CRC) code for post-processing is proposed in [19] [20] [21]. Error floor lowering methods show different levels of success with handcrafted heuristics.…”

Section: A Motivationmentioning

confidence: 99%

DEEPTURBO: Deep Turbo Decoder

Jiang

Kannan

Kim

et al. 2019

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Present-day communication systems routinely use codes that approach the channel capacity when coupled with a computationally efficient decoder. However, the decoder is typically designed for the Gaussian noise channel, and is known to be sub-optimal for non-Gaussian noise distribution. Deep learning methods offer a new approach for designing decoders that can be trained and tailored for arbitrary channel statistics. We focus on Turbo codes, and propose (DEEPTURBO), a novel deep learning based architecture for Turbo decoding.The standard Turbo decoder (TURBO) iteratively applies the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm with an interleaver in the middle. A neural architecture for Turbo decoding, termed (NEURALBCJR), was proposed recently. There, the key idea is to create a module that imitates the BCJR algorithm using supervised learning, and to use the interleaver architecture along with this module, which is then fine-tuned using end-to-end training. However, knowledge of the BCJR algorithm is required to design such an architecture, which also constrains the resulting learnt decoder. Here we remedy this requirement and propose a fully end-to-end trained neural decoder -Deep Turbo Decoder (DEEPTURBO). With novel learnable decoder structure and training methodology, DEEPTURBO reveals superior performance under both AWGN and non-AWGN settings as compared to the other two decoders -TURBO and NEURALBCJR. Furthermore, among all the three, DEEPTURBO exhibits the lowest error floor.

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Lowering the Error Floor of Turbo Codes With CRC Verification

Cited by 27 publications

References 14 publications

Extended Barrel-Shifter for Versatile QC-LDPC Decoders

Extended Barrel-Shifter for Versatile QC-LDPC Decoders

Network for hypersonic UCAV swarms

DEEPTURBO: Deep Turbo Decoder

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