Successive-cancellation list (SCL) decoding of polar codes has been adopted for 5G. However, the performance is not 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 is to accurately identify error bit positions. In this work, we propose a new flip decoding algorithm with help of differentiable neural computer (DNC). New action and state 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 multi-bit flipping fails, a flip-validate DNC (FV-DNC) is used to re-select error positions and assist single-bit flipping successively. 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 0.21dB frame-error-rate (FER) improvement or 45.7% reduction in number of decoding attempts compared to prior works.

翻译：已经为 5G 通过了极地代码的连续取消列表(SCL) 解码 。 但是, 性能不令人满意, 代码长度不高。 已经开发了超速或深学习辅助( DL 辅助) 翻转算法来解决这个问题。 成功翻转的关键是准确识别错误位位置。 在这项工作中, 我们提出一个新的翻转解码算法, 帮助不同的神经神经计算机( DNC ) 。 为了提高 DNC 培训和推断效率, 开发了新的行动和州编码。 提议的方法由两个阶段组成 : i) 翻转 DNC (F- DNC) 用于将多位翻转最有可能的翻转位置排序; ii) 如果多位翻转失败, 翻转码 DNC (FV- DNC ) 用于重新选择错误位置, 协助单位翻转动。 因此, 为两个DNC 国家中心设计了超级培训方法。 模拟结果显示, 拟议的DNC 帮助 SC- Flip (D- SC- Sclip (D- Sc) SC- SC- SC- LF) 7) 将45Reving degreging reglement redududududududududududustring