A Reinforcement Learning Based Decoding Method of Short Polar Codes
Jian Shu Gao, Kai Niu · 2021
In this paper, reinforcement learning (RL) is applied to find effective decoding strategies for short polar codes. We first illustrate how to map the step-decision decoding to a Markov decision process. Then, we choose the signal reliability to formulate the reward strategy, and the path metrics are taken as the Q-values of the state-action pairs. Finally, an adaptive Q-table approach is proposed for data-driven learning of optimal decision strategies. Compared with conventional Q-learning and deep Q-learning networks, the adaptive Q-table results in low computational and storage complexity. Simulation results show that the proposed RL decoding with adaptive Q-table achieves comparable performance to learned bit-flipping decoding and SC decoding with low complexity.