  Maximum Likelihood (ML) decoding of forward error correction codes is known
to be optimally accurate, but is not used in practice as it proves too
challenging to efficiently implement. Here we introduce a ML decoder called
SGRAND, which is a development of a previously described hard detection ML
decoder called GRAND, that fully avails of soft detection information and is
suitable for use with any arbitrary high-rate, short-length block code. We
assess SGRAND's performance on CRC-aided Polar (CA-Polar) codes, which will be
used for all control channel communication in 5G NR, comparing its accuracy
with CRC-Aided Successive Cancellation List decoding (CA-SCL), a
state-of-the-art soft-information decoder specific to CA-Polar codes.
