This paper presents an enhanced belief propagation (BP) decoding algorithm and a reinforcement learning-based BP decoding algorithm for polar codes. The enhanced BP algorithm weighs each Processing Element (PE) input based on their signals and Euclidean distances using a heuristic metric. The proposed reinforcement learning-based BP decoding strategy relies on reweighting the messages and consists of two steps: we first weight each PE input based on their signals and Euclidean distances using a heuristic metric, then a Q-learning algorithm (QLBP) is employed to figure out the best correction factor for successful decoding. Simulations show that the proposed enhanced BP and QLBP decoders outperform the successive cancellation (SC) and belief propagation (BP) decoders, and approach the SCL decoders.

翻译：本文介绍了一种强化的信仰传播(BP)解码算法和一种强化的极地代码基于学习的BP解码算法。强化的BP算法根据信号和超光速度量度衡量了每个处理元素输入的精度。拟议的强化学习基础BP解码战略依赖于对信息进行重新加权,由两步组成:我们首先根据每个PE输入的信号加权,然后使用超光度度度度测算,然后使用Q学习算法(QLBP)来计算成功解码的最佳校正系数。模拟显示,拟议的增强的BP和QLBP解码器比连续取消(SC)和信仰传播(BP)解码器(BP)和接近SCL解码器。