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

• Published in: Entropy (Basel, Switzerland) Vol. 22; no. 5; p. 496
• Main Authors: Lee, Hyunjae, Seo, Eun Young, Ju, Hyosang, Kim, Sang-Hyo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25-04-2020; MDPI
• Subjects: Algorithms; Codes; Compatibility; Decoders; Deep learning; Error correction & detection; Learning; Low density parity check codes; Methods; neural network decoder; Neural networks; Noise; polar codes; Training; transfer learning
• ISSN: 1099-4300; 1099-4300
• 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.