Successive-cancellation list (SCL) decoding of polar codes has been adopted for 5G. However, the performance is not very satisfactory with moderate code length. Heuristic or deep-learning-aided (DL-aided) flip algorithms have been developed to tackle this problem. The key for successful flip decoding is to accurately identify error bit positions. In this work, we propose a new flip algorithm with help of differentiable neural computer (DNC). New state and action encoding are developed for better DNC training and inference efficiency. The proposed method consists of two phases: i) a flip DNC (F-DNC) is exploited to rank most likely flip positions for multi-bit flipping; ii) if decoding still fails, a flip-validate DNC (FV-DNC) is used to re-select error bit positions for successive flip decoding trials. Supervised training methods are designed accordingly for the two DNCs. Simulation results show that proposed DNC-aided SCL-Flip (DNC-SCLF) decoding demonstrates up to 0.34dB coding gain improvement or 54.2% reduction in average number of decoding attempts compared to prior works.

翻译：已经为 5G 通过了连续取消代码列表(SCL ) 。 但是, 性能在代码长度中并不非常令人满意。 为了解决这一问题, 已经开发了超度或深学习辅助翻转算法( DL 辅助) 。 成功翻转解码的关键是准确识别误差位位。 在这项工作中, 我们提出一个新的翻转算法, 帮助不同的神经计算机( DNC) 。 开发了新的状态和行动编码, 以提高 DNC 培训和推断效率。 提议的方法包括两个阶段 : i) 翻转 DNC (F- DNC) 被开发为多位翻转的最有可能的翻转位置排序; ii) 如果解码失败, 则使用翻转法解码 DNC (FV- DNC) 来为连续翻转解码试验重新选择误差位位。 超视培训方法是针对两个DNC CNS 的。 模拟结果显示, 拟议的DNC 帮助 SC- Flip (D NC- DNC- SC) 的翻校略(DNC) CO 尝试显示, 正在逐步递减到 平均衰减为0.34 。