Polar codes are the first error-correcting code proven to achieve channel capacity based on infinite code length. The Successive Cancellation List Flip (SCLF) decoding algorithm was proposed by flipping an erroneous bit during the next decoding attempt. To identify the erroneous bits, the Log-Likelihood Ratio (LLR) is used to indicate the reliability of each decision bit. To improve the accuracy of the erroneous bit prediction, we propose deep-learning-aided (DL-aided) SCLF decoding algorithms. We first offer a stacked LSTM network that contains new features to train our models, which are able to improve the accuracy of the prediction of positions of erroneous bits. Then we separately train the stacked LSTM models to predict the position of both the first and second erroneous bits and whether to continue flipping. As a result, the DL-aided SCLF decoding algorithms based on the proposed stacked LSTM \mbox{flip-1} model, stacked LSTM \mbox{flip-2} model, and the stacked LSTM \mbox{continue-flipping} check (CFC) model are able to provide a better performance at a lower number of average decoding attempts when compared to other state-of-the-art decoding algorithms.

翻译：极化码是首个被证明基于无限码长可达信道容量的纠错码。逐次消除列表翻转（SCLF）译码算法通过在下一次译码尝试中翻转错误比特来实现译码。为识别错误比特，采用对数似然比（LLR）指示各判决比特的可靠性。为提高错误比特预测的准确性，我们提出了深度学习辅助（DL-aided）的SCLF译码算法。首先构建包含新特征的堆叠长短期记忆（LSTM）网络以训练模型，该模型能提升错误比特位置预测的准确性。随后分别训练堆叠LSTM模型以预测首个与第二个错误比特的位置，并判断是否继续翻转。实验表明，基于所提堆叠LSTM翻转-1模型、堆叠LSTM翻转-2模型及堆叠LSTM继续翻转检查（CFC）模型的DL-aided SCLF译码算法，在保持较低平均译码尝试次数的同时，相较于其他先进译码算法展现出更优性能。