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- Title
High Speed Decoding for High-Rate and Short-Length Reed–Muller Code Using Auto-Decoder.
- Authors
Cho, Hyun Woo; Song, Young Joon
- Abstract
In this paper, we show that applying a machine learning technique called auto-decoder (AD) to high-rate and short length Reed–Muller (RM) decoding enables it to achieve maximum likelihood decoding (MLD) performance and faster decoding speed than when fast Hadamard transform (FHT) is applied in additive white Gaussian noise (AWGN) channels. The decoding speed is approximately 1.8 times and 125 times faster than the FHT decoding for R (1 , 4) and R (2 , 4) , respectively. The number of nodes in the hidden layer of AD is larger than that of the input layer, unlike the conventional auto-encoder (AE). Two ADs are combined in parallel and merged together, and then cascaded to one fully connected layer to improve the bit error rate (BER) performance of the code.
- Subjects
REED-Muller codes; ADDITIVE white Gaussian noise; HADAMARD matrices; BIT error rate; MACHINE learning; SPEED
- Publication
Applied Sciences (2076-3417), 2022, Vol 12, Issue 18, p9225
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app12189225