Solomon, Amit and Duffy, Ken R. and Medard, Muriel (2020) Soft Maximum Likelihood Decoding using GRAND. In: IEEE International Conference on Communications (ICC), 7-11 June 2020, Dublin, Ireland.

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Abstract

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 propose a development of a previously described hard detection ML decoder called Guessing Random Additive Noise Decoding (GRAND). We introduce Soft GRAND (SGRAND), a ML decoder 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 Cyclic Redundancy Check (CRC)-aided Polar (CA-Polar) codes, which will be used for all control channel communication in 5G New Radio (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.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	ML decoding; GRAND; 5G NR; CA-Polar;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15280
Identification Number:	https://doi.org/10.1109/ICC40277.2020.9149208
Depositing User:	Dr Ken Duffy
Date Deposited:	19 Jan 2022 12:13
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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