Construction of Polar Codes with Reinforcement Learning

Yun Liao, Seyyed Ali Hashemi, John M. Cioffi, Andrea Goldsmith

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


This paper formulates the polar-code construction problem for the successive-cancellation list (SCL) decoder as a maze-traversing game, which can be solved by reinforcement-learning techniques. The proposed method provides a novel technique for polar-code construction that no longer depends on sorting and selecting bit-channels by reliability, as in most current algorithms. Instead, this technique decides whether the input bits should be frozen in a purely sequential manner. The equivalence of optimizing the polar-code construction for the SCL decoder under this technique and maximizing the expected reward of traversing a maze is drawn. Simulation results show that the standard polar-code constructions that are designed for the successive-cancellation decoder are no longer optimal for the SCL decoder with respect to the frame error rate (FER). In contrast, the proposed game-based construction method finds code constructions that have similar or lower FER for various code lengths and various list sizes of the SCL decoder, compared to the state-of-the-art construction methods. The advantage of the game-based constructions over the standard constructions increases with the channel signal-to-noise ratio and the list size of SCL decoding. Moreover, the learning is highly efficient in terms of the number of required training samples and computational operations.

Original languageEnglish (US)
Pages (from-to)185-198
Number of pages14
JournalIEEE Transactions on Communications
Issue number1
StatePublished - Jan 1 2022

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


  • Polar-code design
  • reinforcement learning
  • successive-cancellation list decoding


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