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Guessing Random Additive Noise Decoding (GRAND) is a universal decodingalgorithm that can be used to perform maximum likelihood decoding. It attemptsto find the errors introduced by the channel by generating a sequence ofpossible error vectors in order of likelihood of occurrence and applying themto the received vector. Ordered reliability bits GRAND (ORBGRAND) integratessoft 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 useas an iterative soft-input soft-output (SISO) decoder. Three techniquesspecific to iterative GRAND-based decoding are then proposed to improve theerror-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 of48\% to 85\% with respect to the baseline SISO ORBGRAND.

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