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Improved NSC decoding algorithm for polar codes based on multi-in-one neural network

机译：基于多联网神经网络的极性代码改进了NSC解码算法

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摘要

Neural network-based decoding algorithms have potential value to be researched in the field of polar codes due to their low decoding latency. The neural successive cancellation (NSC) algorithm, combining deep learning and successive cancellation (SC) algorithm of polar codes, was proposed to reduce the latency of decoding. In terms of overall latency, the NSC algorithm does not fully consider the parallel decoding of special nodes in SC decoding tree, which limits the reduction of system delay to a certain extent. In this paper, we propose a multi-in-one neural simplified successive cancellation (MIO-NSSC) decoding algorithm for polar codes based on deep learning. The proposed MIO-NSSC algorithm, which is suitable for general nodes, mainly improves the existing fast simplified successive cancellation (FSSC) and the NSC algorithms to obtain a multi-in-one neural network instead of multiple neural networks in the NSC algorithm by using a new training strategy. Through applying the FSSC algorithm to a special node, the decoding delay of the proposed algorithm is further reduced. The experimental results demonstrate that the proposed MIO-NSSC algorithm can achieve significant latency reduction and resource consumption efficiency improvement compared with the NSC algorithm. The latency of the proposed MIO-NSSC decoding algorithm is about 21% lower than that of the NSC algorithm, and approximately seven neural networks are saved compared with the NSC algorithm. Furthermore, the MIO-NSSC algorithm can reduce the computational complexity without loss of performance. (C) 2020 Elsevier Ltd. All rights reserved.
机译：基于神经网络的解码算法具有潜在的值，该潜在值是由于它们的低解码延迟而在极性代码领域中研究。建议北极代码的神经连续取消（NSC）算法，组合深度学习和连续消除（SC）算法，以降低解码的延迟。在整体延迟方面，NSC算法没有完全考虑SC解码树中特殊节点的并行解码，这限制了系统延迟的降低到一定程度。在本文中，我们提出了一种基于深度学习的极性代码的多对神经简化的连续消除（MIO-NSSC）解码算法。所提出的MIO-NSSC算法，适用于一般节点，主要是通过使用来获得现有快速简化的连续取消（FSSC）和NSC算法，而不是使用NSC算法中的多个神经网络而不是NSC算法中的多个神经网络新的培训策略。通过将FSSC算法应用于特殊节点，进一步减少了所提出的算法的解码延迟。实验结果表明，与NSC算法相比，所提出的MIO-NSSC算法可以实现显着降低和资源消耗效率改进。所提出的MIO-NSSC解码算法的延迟比NSC算法的算法低约21％，与NSC算法相比，节省了大约七个神经网络。此外，MIO-NSSC算法可以减少计算复杂性而不会损失性能。 （c）2020 elestvier有限公司保留所有权利。

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