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Open AccessArticle

On Training Neural Network Decoders of Rate Compatible Polar Codes via Transfer Learning

1
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea
2
Samsung Electronics, Hwaseong 18448, Korea
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(5), 496; https://doi.org/10.3390/e22050496
Received: 3 March 2020 / Revised: 19 April 2020 / Accepted: 22 April 2020 / Published: 25 April 2020
(This article belongs to the Special Issue Information Theory for Communication Systems)
Neural network decoders (NNDs) for rate-compatible polar codes are studied in this paper. We consider a family of rate-compatible polar codes which are constructed from a single polar coding sequence as defined by 5G new radios. We propose a transfer learning technique for training multiple NNDs of the rate-compatible polar codes utilizing their inclusion property. The trained NND for a low rate code is taken as the initial state of NND training for the next smallest rate code. The proposed method provides quicker training as compared to separate learning of the NNDs according to numerical results. We additionally show that an underfitting problem of NND training due to low model complexity can be solved by transfer learning techniques. View Full-Text
Keywords: polar codes; deep learning; neural network decoder; transfer learning polar codes; deep learning; neural network decoder; transfer learning
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Lee, H.; Seo, E.Y.; Ju, H.; Kim, S.-H. On Training Neural Network Decoders of Rate Compatible Polar Codes via Transfer Learning. Entropy 2020, 22, 496.

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