Lee Hyunjae, Seo Eun Young, Ju Hyosang, Kim Sang-Hyo
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea.
Samsung Electronics, Hwaseong 18448, Korea.
Entropy (Basel). 2020 Apr 25;22(5):496. doi: 10.3390/e22050496.
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.
本文研究了用于速率兼容极化码的神经网络解码器（NND）。我们考虑一族速率兼容极化码，它们由5G新无线电所定义的单个极化编码序列构建而成。我们提出一种迁移学习技术，用于利用速率兼容极化码的包含特性来训练多个NND。低速率码的训练好的NND被用作下一个最小速率码的NND训练的初始状态。根据数值结果，与单独学习NND相比，所提出的方法提供了更快的训练。我们还表明，迁移学习技术可以解决由于模型复杂度低导致的NND训练欠拟合问题。
