We propose a self-supervised deep learning-based decoding scheme that enables one-shot decoding of polar codes. In the proposed scheme, rather than using the information bit vectors as labels for training the neural network (NN) through supervised learning as the conventional scheme did, the NN is trained to function as a bounded distance decoder by leveraging the generator matrix of polar codes through self-supervised learning. This approach eliminates the reliance on predefined labels, empowering the potential to train directly on the actual data within communication systems and thereby enhancing the applicability. Furthermore, computer simulations demonstrate that (i) the bit error rate (BER) and block error rate (BLER) performances of the proposed scheme can approach those of the maximum a posteriori (MAP) decoder for very short packets and (ii) the proposed NN decoder (NND) exhibits much superior generalization ability compared to the conventional one.

翻译：我们提出了一种基于自监督深度学习的译码方案，能够实现极化码的一次性译码。在该方案中，与常规方法通过监督学习将信息比特向量作为标签来训练神经网络不同，神经网络通过自监督学习利用极化码的生成矩阵进行训练，使其具备有界距离译码器的功能。这种方法消除了对预定义标签的依赖，使得能够在通信系统内直接对实际数据进行训练，从而增强了适用性。此外，计算机仿真结果表明：(i) 对于极短数据包，所提方案的误码率和误块率性能可接近最大后验概率译码器；(ii) 与常规神经网络译码器相比，所提神经网络译码器展现出更优越的泛化能力。