论文标题
通过增强学习解码极地代码
Decoding Polar Codes with Reinforcement Learning
论文作者
论文摘要
在本文中，我们解决了在信仰传播（BP）中选择因子图形置换（BP）的问题，以显着改善代码的误差校正性能。特别是，我们将因子图形置换选择形式化为增强学习中的多臂强盗问题，并提出了一个像在线学习代理一样的解码器，该解码器学会在解码过程中选择良好的因子钻机排列。我们在多武器的强盗问题上使用最先进的算法，并表明，对于具有64个信息位的5G极长128的5G极性代码，在目标框架误差率的10^{-4}的情况下，与随机选择的方法相比，在目标框架误差率下，拟议的解码器的错误校正性能增益约为0.125 db。
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.
