In this work, a novel data-driven methodology for designing polar codes for channels with and without memory is proposed. The methodology is suitable for the case where the channel is given as a "black-box" and the designer has access to the channel for generating observations of its inputs and outputs, but does not have access to the explicit channel model. The proposed method leverages the structure of the successive cancellation (SC) decoder to devise a neural SC (NSC) decoder. The NSC decoder uses neural networks (NNs) to replace the core elements of the original SC decoder, the check-node, the bit-node and the soft decision. Along with the NSC, we devise additional NN that embeds the channel outputs into the input space of the SC decoder. The proposed method is supported by theoretical guarantees that include the consistency of the NSC. Also, the NSC has computational complexity that does not grow with the channel memory size. This sets its main advantage over successive cancellation trellis (SCT) decoder for finite state channels (FSCs) that has complexity of $O(|\mathcal{S}|^3 N\log N)$, where $|\mathcal{S}|$ denotes the number of channel states. We demonstrate the performance of the proposed algorithms on memoryless channels and on channels with memory. The empirical results are compared with the optimal polar decoder, given by the SC and SCT decoders. We further show that our algorithms are applicable for the case where there SC and SCT decoders are not applicable.

通过新颖的数据驱动方法，提出了一种针对具有和不具有记忆的信道设计极化码的方法。该方法利用连续取消 (SC) 译码器的结构来设计神经 SC (NSC) 译码器，使用神经网络 (NNs) 替代原始 SC 译码器的核心元素，以及在 SC 译码器的输入空间中嵌入信道输出的附加 NN。该方法不仅具备理论保证，还具有计算复杂性随信道记忆大小而不增长的优势。该算法在无记忆信道和具有记忆信道上展示了其性能，并与最优极化译码器进行了比较，同时还适用于 SC 和 SCT 译码器不适用的情况。

基于数据驱动的神经极化码研究未知无记忆信道及带有记忆信道