  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.
