Construction of Polar Codes with Reinforcement Learning

• URL: http://arxiv.org/abs/2009.09277v1
• Date: Sat, 19 Sep 2020 17:59:02 GMT
• Title: Construction of Polar Codes with Reinforcement Learning
• Authors: Yun Liao, Seyyed Ali Hashemi, John Cioffi, Andrea Goldsmith
• Abstract summary: This paper formulates the polar-code construction problem for the successive-cancellation list (SCL) decoder as a maze-traversing game. The proposed method provides a novel technique for polar-code construction that no longer depends on sorting and selecting bit-channels by reliability.
• Score: 13.977646909897796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. 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. In contrast, the simulations show that, with a reasonable amount of training, the game-based construction method finds code constructions that have lower frame-error rate for various code lengths and decoders compared to standard constructions.

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