神经计算（Neural Computation）期刊传播在理论、建模、计算方面的重要的多学科的研究，在神经科学统计和建设神经启发信息处理系统。这个领域吸引了心理学家、物理学家、计算机科学家、神经科学家和人工智能研究人员，他们致力于研究感知、情感、认知和行为背后的神经系统，以及具有类似能力的人工神经系统。由BRAIN Initiative开发的强大的新实验技术将产生大量复杂的数据集，严谨的统计分析和理论洞察力对于理解这些数据的含义至关重要。及时的、简短的交流、完整的研究文章以及对该领域进展的评论，涵盖了神经计算的所有方面。
官网地址：http://dblp.uni-trier.de/db/journals/neco/
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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.
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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.
