Stochastic Chase Decoding of Reed-Solomon Codes

Leroux, Camille; Hemati, Saied; Mannor, Shie; Gross, Warren J.

doi:10.1109/lcomm.2010.09.100594

Cited by 18 publications

(16 citation statements)

References 12 publications

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“…In its original form, Chase decoding incurs the complexity of performing hard decoding multiplied by a factor that is exponential in the number of unreliable bits. Improvements to this approach make use of the stochastic nature of errors to enhance performance [34], [35], [36], [37], [38], [4], but with improvement of the order of halving, on average, the number of operations. A thorough overview of Chase decoding for BCH and Reed-Solomon codes, and of the recent developments in incorporating soft information in decoding, is provided in [4].…”

Section: Related Workmentioning

confidence: 99%

Guessing random additive noise decoding with soft detection symbol reliability information - SGRAND

Duffy

Médard

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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In computer communications, discrete data are first channel coded and then modulated into continuous signals for transmission and reception. In a hard detection setting, only demodulated data are provided to the decoder. If soft information on received signal quality is provided, its use can improve decoding accuracy. Incorporating it, however, typically comes at the expense of increased algorithmic complexity. Here we introduce a mechanism to use binarized soft information in the Guessing Random Additive Noise Decoding framework such that decoding accuracy is increased, but computational complexity is decreased. The principle envisages a code-book-independent quantization of soft information where demodulated symbols are additionally indicated to be reliable or unreliable. We introduce two algorithms that incorporate this information, one of which identifies a Maximum Likelihood (ML) decoding and the other either reports an ML decoding or an error. Both are suitable for use with any block-code, and are capacityachieving. We determine error exponents and asymptotic complexity. They achieve higher rates with lower error probabilities and less algorithmic complexity than their hard detection counterparts. As practical illustrations, we compare performance with majority logic decoding of a Reed-Muller code, with Berlekamp-Massey decoding of a BCH code, and establish performance of Random Linear Codes.

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Section: Related Workmentioning

confidence: 99%

Guessing random additive noise decoding with soft detection symbol reliability information - SGRAND

Duffy

Médard

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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“…The soft input for the IEED decoder is the reliability of each bit, which can be represented in the probability domain as [11],…”

Section: Model Of the Communication System And Notations Used In The mentioning

confidence: 99%

“…The log likelihood ratio (LLR) for the bit i, j in the received sequence is given by [11] r i,j = L(x i,j |y i,j ) = log P(x i,j = 1|y i,j ) P(x i,j = −1|y i,j ) ,…”

Section: Model Of the Communication System And Notations Used In The mentioning

confidence: 99%

Soft decision iterative error and erasure decoder for Reed–Solomon codes

Ur-Rehman

Živić

2014

IET Communications

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A list decoding algorithm for iterative soft-decision decoding of Reed-Solomon (RS) codes is proposed in this study. The algorithm erases a combination of least reliable symbols and iteratively flips a new combination of least reliable bits in the received sequence. The symbol error correction capability of the algebraic hard decision RS decoder is extended up to the minimum Hamming distance of the code. A gain in performance is exhibited either at a lower or similar iteration complexity as compared to other iterative soft decision decoders of similar type. A complexity comparison with some of the comparable soft decision decoders is given. Simulation results for different modulation schemes and channel types are presented in this study for a comparison with other soft decision decoders of a similar kind.

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“…In [11], it was shown that this algorithm provides better performance with a reduced number of test vectors compared to the Chase algorithm. From a hardware implementation perspective, Step 2 can be implemented by using a single decoding unit and reusing it for each test vector.…”

Section: Stochastic Chase Decoding Algorithmmentioning

confidence: 99%

“…In terms of hardware complexity, the least reliable bits selection and the test vector construction take a non-negligible part of the decoder. In [11], a stochastic version of the Chase algorithm was proposed. It allows the number of test vectors to be reduced and the least reliable bits selection to be replaced by independent stochastic units.…”

Section: Introductionmentioning

confidence: 99%

Stochastic chase decoder for reed-solomon codes

Heloir

Leroux

Hemati

et al. 2012

10th IEEE International NEWCAS Conference

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This paper presents a hardware implementation of a soft-decision Reed-Solomon (RS) decoder, based on the stochastic Chase algorithm. This decoder achieves a coding gain of at least 0.45 dB at FER=10 −4 for the well-known RS(255,239) code. To the best of our knowledge this is the highest reported coding gain for a hardware implementation of soft-decision RS(255,239) decoder. The decoder is implemented on a Virtex-5 FPGA and works at 800 Mb/s. The proposed decoder has a flexible architecture and is well suited for applications requiring different throughputs and decoding performance.

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Stochastic Chase Decoding of Reed-Solomon Codes

Cited by 18 publications

References 12 publications

Guessing random additive noise decoding with soft detection symbol reliability information - SGRAND

Guessing random additive noise decoding with soft detection symbol reliability information - SGRAND

Soft decision iterative error and erasure decoder for Reed–Solomon codes

Stochastic chase decoder for reed-solomon codes

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