Deep learning recently shows outstanding potential in channel decoding optimization, but its effect on the decoding of Reed-Solomon (RS) codes has yet to be explored. In this paper, we propose a RS decoder based on deep learning for the first time, and pave a new way to improve the existing RS decoding algorithms. We exploit a deep neural network (DNN) to estimate the error numbers of the received codewords, and according to the estimation results, a novel decoder is designed, which can adjust the most suitable decoding method to each received codeword automatically. Experiments show that for (7, 3), (15, 9) and (63, 55) RS codes, the average computational complexity of our decoder can be reduced by 68.96 %, 62.38 %, 50.61 % respectively compared with the HDD-LCC algorithm.
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High-Efficient Reed-Solomon Decoder Based on Deep Learning
Semantic Scholar · Computer Science · 2020
Abstract
Deep learning recently shows outstanding potential in channel decoding optimization, but its effect on the decoding of Reed-Solomon (RS) codes has yet to be explored. In this paper, we propose a RS decoder based on deep learning for the first time, and pave a new way to improve the existing RS decoding algorithms. We exploit a deep neural network (DNN) to estimate the error numbers of the received codewords, and according to the estimation results, a novel decoder is designed, which can adjust the most suitable decoding method to each received codeword automatically. Experiments show that for (7, 3), (15, 9) and (63, 55) RS codes, the average computational complexity of our decoder can be reduced by 68.96 %, 62.38 %, 50.61 % respectively compared with the HDD-LCC algorithm.