论文标题
通过增强学习的构建极地代码
Construction of Polar Codes with Reinforcement Learning
论文作者
论文摘要
本文为连续的策略列表(SCL)解码器制定了极地代码构建问题,作为一种迷宫的吸引游戏,可以通过增强学习技术来解决。所提出的方法为极地代码构建提供了一种新型技术,不再取决于通过可靠性进行分类和选择位通道。取而代之的是,该技术决定是否应纯粹顺序冻结输入位。在该技术下优化SCL解码器的极性代码结构并最大程度地提高迷宫迷宫的预期奖励的等效性。仿真结果表明,专为连续策略解码器设计的标准极性代码构建体不再是SCL解码器相对于帧错误率的最佳选择。相比之下,模拟表明,通过合理的培训,基于游戏的构造方法找到了与标准构造相比,各种代码长度和解码器的帧率率较低的代码结构。
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.