Soft Maximum Likelihood Decoding using GRAND
Name
2001.03089.pdf
Description
Submitted version
Size
428.14 KB
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Unknown
Checksum (MD5)
55d9c947ca30da92c9a1aa2ffaade920
Author(s)
Solomon, Amit
Duffy, Ken R.
Medard, Muriel
Date Issued
June 2020
Journal
IEEE International Conference on Communications
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
2020. "Soft Maximum Likelihood Decoding using GRAND." IEEE International Conference on Communications, 2020-June.
Version
Original manuscript
Abstract
© 2020 IEEE. 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-theart soft-information decoder specific to CA-Polar codes.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICC40277.2020.9149208
