Neural layered min-sum decoders for cyclic codes

This paper proposes a low-complexity neural network decoder based on the layered min-sum algorithm to decode cyclic codes. By generalizing the layered min-sum algorithm to its neural network counterpart, the number of network weights decreases while retaining a good error correction performance. The...

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Bibliographic Details
Published in:	Physical communication Vol. 61; p. 102194
Main Authors:	Wang, Ming, Li, Yong, Liu, Jianqing, Guo, Taolin, Wu, Huihui, Lau, Francis C.M.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2023
Subjects:	Channel codes Layered min-sum algorithm Modified random redundant decoding Neural network decoders Modified random redundant decoding Neural network decoders Channel codes Layered min-sum algorithm
ISSN:	1874-4907
Online Access:	Get full text
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Summary:	This paper proposes a low-complexity neural network decoder based on the layered min-sum algorithm to decode cyclic codes. By generalizing the layered min-sum algorithm to its neural network counterpart, the number of network weights decreases while retaining a good error correction performance. The Bose–Chaudhuri–Hocquenghem (BCH) codes, quadratic residue (QR) codes, and punctured Reed–Muller (RM) codes are selected as three exemplary binary cyclic codes. Simulation results show that the proposed neural decoder achieves superior performance with less computational complexity compared with the state-of-the-art neural network decoder. Further, a neural decoder incorporating the modified random redundant decoding (mRRD) algorithm is investigated to approach the performance of maximum-likelihood decoding for some short codes.
ISSN:	1874-4907
DOI:	10.1016/j.phycom.2023.102194