Block turbo decoding with ORBGRAND
Kevin Galligan, Muriel Médard, Ken R. Duffy
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.
Topics & Concepts
Decoding methodsComputer scienceList decodingConcatenated error correction codeSequential decodingTurbo codeAlgorithmRedundancy (engineering)Block codeSerial concatenated convolutional codesBerlekamp–Welch algorithmTheoretical computer scienceOperating systemAdvanced Wireless Communication TechniquesError Correcting Code TechniquesCoding theory and cryptography