Block turbo decoding with ORBGRAND

TL;DR

This paper introduces ORBGRAND, a universal decoding algorithm that, when used in turbo decoding of product codes, outperforms traditional Chase algorithms in decoding efficiency and flexibility.

Contribution

It demonstrates that soft-input variants of GRAND can replace Chase in turbo decoding, enabling decoding of arbitrary product codes with improved coding gain.

Findings

ORBGRAND achieves up to 0.7dB gain over Chase algorithm.

ORBGRAND can decode any product code, not just those with dedicated decoders.

Soft-input GRAND variants effectively replace Chase in turbo decoding.

Abstract

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 0.7dB over the Chase algorithm with same list size.