IGRAND: decode any product code

Galligan, Kevin; Solomon, Amit; Riaz, Arslan; Médard, Muriel; Yazicigil, Rabia Tugce; Duffy, Ken R.

doi:10.1109/globecom46510.2021.9685645

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…Fig. 5 shows the list decoding accuracy of a further selection of codes with basic ORBGRAND as the decoder: eBCH(32, 26, 4), RLC(32, 26, 3) and CRC (31,26,3). The CRC polynomial is 0x12 in Koopman notation and comes from [26].…”

Section: A List Decoding Accuracymentioning

confidence: 99%

“…GRAND is a recently introduced family of hard [1][2][3] and soft [4][5][6][7] decoding algorithms that can accurately decode arbritrary codes, even non-linear ones [8], with a moderate number of redundant bits. Their promise for practical, highlyparallelised decoding has led to the publication of several circuit designs and a chip implementation [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Product codes are a class of long, high redundancy codes that are constructed from a concatenation of shorter component codes. Iterative GRAND (IGRAND) [3] adapts GRAND for hard-input iterative decoding of product codes [14]. The current paper considers how GRAND can decode product codes with the aid of soft information.…”

Section: Introductionmentioning

confidence: 99%

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Block turbo decoding with ORBGRAND

Galligan¹,

Médard²,

Duffy³

2022

Preprint

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Guessing Random Additive Noise Decoding (GRAND) is a family of universal decoding algorithms suitable for decoding any moderate redundancy code of any length. We establish that, through the use of list decoding, soft-input variants of GRAND can replace the Chase algorithm as the component decoder in the turbo decoding of product codes. In addition to being able to decode arbitrary product codes, rather than just those with dedicated hard-input component code decoders, results show that ORBGRAND achieves a coding gain of up to 0.2dB over the Chase algorithm as a turbo component decoder, for a selection of product codes.

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Section: A List Decoding Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Block turbo decoding with ORBGRAND

Galligan¹,

Médard²,

Duffy³

2022

Preprint

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“…While it is possible to iterate HIHO decoders, SISO iterative decoding substantially improves error-correction performance with a limited number of iterations, and helps lowering error floors. GRAND has been applied as an HIHO decoder for turbo product codes in [17], but no SISO decoder based on GRAND has been proposed so far.…”

Section: Iterative Siso Decoding With Orbgrandmentioning

confidence: 99%

Iterative Soft-Input Soft-Output Decoding with Ordered Reliability Bits GRAND

Condo¹

2022

Preprint

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Guessing Random Additive Noise Decoding (GRAND) is a universal decoding algorithm that can be used to perform maximum likelihood decoding. It attempts to find the errors introduced by the channel by generating a sequence of possible error vectors in order of likelihood of occurrence and applying them to the received vector. Ordered reliability bits GRAND (ORBGRAND) integrates soft information received from the channel to refine the error vector sequence. In this work, ORBGRAND is modified to produce a soft output, to enable its use as an iterative soft-input soft-output (SISO) decoder. Three techniques specific to iterative GRAND-based decoding are then proposed to improve the error-correction performance and decrease computational complexity and latency. Using the OFEC code as a case study, the proposed techniques are evaluated, yielding substantial performance gain and astounding complexity reduction of 48% to 85% with respect to the baseline SISO ORBGRAND.

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