In this paper we address the problem of selecting factor-graph permutations of polar codes under belief propagation (BP) decoding to significantly improve the error-correction performance of the code. In particular, we formalize the factor-graph permutation selection as the multi-armed bandit problem in reinforcement learning and propose a decoder that acts like an online-learning agent that learns to select the good factor-graph permutations during the course of decoding. We use state-of-the-art algorithms for the multi-armed bandit problem and show that for a 5G polar codes of length 128 with 64 information bits, the proposed decoder has an error-correction performance gain of around 0.125 dB at the target frame error rate of 10^{-4}, when compared to the approach that randomly selects the factor-graph permutations.

中文翻译：

---

使用强化学习解码 Polar 码

在本文中，我们解决了在置信传播（BP）解码下选择极坐标码的因子图排列的问题，以显着提高码的纠错性能。特别是，我们将因子图排列选择形式化为强化学习中的多臂老虎机问题，并提出了一种解码器，其作用类似于在线学习代理，可在解码过程中学习选择好的因子图排列。我们使用最先进的算法解决多臂老虎机问题，并表明对于长度为 128 且具有 64 个信息位的 5G 极性码，所提出的解码器在目标处的纠错性能增益约为 0.125 dB与随机选择因子图排列的方法相比，帧错误率为 10^{-4}。