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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Vydané v:Physical communication Ročník 61; s. 102194
Hlavní autori: Wang, Ming, Li, Yong, Liu, Jianqing, Guo, Taolin, Wu, Huihui, Lau, Francis C.M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.12.2023
Predmet:
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
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Shrnutí: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