Quantized Guessing Random Additive Noise Decoding

Duffy, Ken R.; Gabhart, Evan P.; Yuan, Peihong; Médard, Muriel

doi:10.48550/arxiv.2203.13552

Cited by 1 publication

(1 citation statement)

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“…In general, for a probability of bit flip less than 1/2, in the absence of soft information or further channel knowledge, noise query follows increasing Hamming weights, as in the case of hard-detection GRAND. Adopting the quantized GRAND algorithm that avails of any level of quantized soft information [25] is also promising with coarse pseudo-soft information.…”

Section: Performance Evaluationmentioning

confidence: 99%

GRAND for Fading Channels using Pseudo-soft Information

Sarieddeen¹,

Médard²,

Duffy³

2022

Preprint

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Guessing random additive noise decoding (GRAND) is a universal maximum-likelihood decoder that recovers codewords by guessing rank-ordered putative noise sequences and inverting their effect until one or more valid code-words are obtained. This work explores how GRAND can leverage additivenoise statistics and channel-state information in fading channels. Instead of computing per-bit reliability information in detectors and passing this information to the decoder, we propose leveraging the colored noise statistics following channel equalization as pseudo-soft information for sorting noise sequences. We investigate the efficacy of pseudo-soft information extracted from linear zero-forcing and minimum mean square error equalization when fed to a hardware-friendly soft-GRAND (ORBGRAND). We demonstrate that the proposed pseudo-soft GRAND schemes approximate the performance of state-of-the-art decoders of CA-Polar and BCH codes that avail of complete soft information. Compared to hard-GRAND, pseudo-soft ORBGRAND introduces up to 10 dB SNR gains for a target 10 −3 block-error rate.

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Section: Performance Evaluationmentioning

confidence: 99%