Duffy, Ken R. and Medard, Muriel (2019) Guessing random additive noise decoding with soft detection symbol reliability information - SGRAND. In: 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, pp. 480-484. ISBN 9781538692912

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Abstract

We recently introduced a noise-centric algorithm, Guessing Random Additive Noise Decoding (GRAND), that identifies a Maximum Likelihood (ML) decoding for arbitrary code-books. GRAND has the unusual property that its complexity decreases as code-book rate increases. Here we provide an extension to GRAND, soft-GRAND (SGRAND), that incorporates soft detection symbol reliability information and identifies a ML decoding in that context. In particular, we assume symbols received from the channel are declared to be error free or to have been potentially subject to additive noise. SGRAND inherits desirable properties of GRAND, including being capacity achieving when used with random code-books, and having a complexity that reduces as the code-rate increases.

Item Type:	Book Section
Additional Information:	Cite as: K. R. Duffy and M. Médard, "Guessing random additive noise decoding with soft detection symbol reliability information - SGRAND," 2019 IEEE International Symposium on Information Theory (ISIT), Paris, France, 2019, pp. 480-484, doi: 10.1109/ISIT.2019.8849297.
Keywords:	Channel Coding; Soft detection; Symbol Reliability; ML decoding; Error exponents;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	14233
Identification Number:	https://doi.org/10.1109/ISIT.2019.8849297
Depositing User:	Dr Ken Duffy
Date Deposited:	23 Mar 2021 16:28
Publisher:	IEEE
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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