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Efficient Maximum-Likelihood Decoding of Reed-Muller RM(m-3,m) Codes

Publication ,  Conference
Thangaraj, A; Pfister, HD
Published in: IEEE International Symposium on Information Theory - Proceedings
June 1, 2020

Reed-Muller (RM) codes, a classical family of codes known for their elegant algebraic structure, have recently been shown to achieve capacity under maximum-likelihood (ML) decoding on the binary erasure channel and this has rekindled interest in their efficient decoding. We consider the code family RM(m-3,m) and develop a new ML decoder, for transmission over the binary symmetric channel, that exploits their large symmetry group. The new decoder has lower complexity than an earlier method introduced by Seroussi and Lempel in 1983.

Duke Scholars

Published In

IEEE International Symposium on Information Theory - Proceedings

DOI

ISSN

2157-8095

ISBN

9781728164328

Publication Date

June 1, 2020

Volume

2020-June

Start / End Page

263 / 268
 

Citation

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ICMJE
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Thangaraj, A., & Pfister, H. D. (2020). Efficient Maximum-Likelihood Decoding of Reed-Muller RM(m-3,m) Codes. In IEEE International Symposium on Information Theory - Proceedings (Vol. 2020-June, pp. 263–268). https://doi.org/10.1109/ISIT44484.2020.9174065
Thangaraj, A., and H. D. Pfister. “Efficient Maximum-Likelihood Decoding of Reed-Muller RM(m-3,m) Codes.” In IEEE International Symposium on Information Theory - Proceedings, 2020-June:263–68, 2020. https://doi.org/10.1109/ISIT44484.2020.9174065.
Thangaraj A, Pfister HD. Efficient Maximum-Likelihood Decoding of Reed-Muller RM(m-3,m) Codes. In: IEEE International Symposium on Information Theory - Proceedings. 2020. p. 263–8.
Thangaraj, A., and H. D. Pfister. “Efficient Maximum-Likelihood Decoding of Reed-Muller RM(m-3,m) Codes.” IEEE International Symposium on Information Theory - Proceedings, vol. 2020-June, 2020, pp. 263–68. Scopus, doi:10.1109/ISIT44484.2020.9174065.
Thangaraj A, Pfister HD. Efficient Maximum-Likelihood Decoding of Reed-Muller RM(m-3,m) Codes. IEEE International Symposium on Information Theory - Proceedings. 2020. p. 263–268.

Published In

IEEE International Symposium on Information Theory - Proceedings

DOI

ISSN

2157-8095

ISBN

9781728164328

Publication Date

June 1, 2020

Volume

2020-June

Start / End Page

263 / 268