By concatenating a polar transform with a convolutional transform, polarization-adjusted convolutional (PAC) codes can reach the dispersion approximation bound in certain rate cases. However, the sequential decoding nature of traditional PAC decoding algorithms results in high decoding latency. Due to the parallel computing capability, deep neural network (DNN) decoders have emerged as a promising solution. In this paper, we propose three types of DNN decoders for PAC codes: multi-layer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The performance of these DNN decoders is evaluated through extensive simulation. Numerical results show that the MLP decoder has the best error-correction performance under a similar model parameter number.

翻译：通过将极化变换与卷积变换级联，极化调整卷积（PAC）码在特定码率条件下可达到色散近似界。然而，传统PAC解码算法的序列解码特性导致较高解码延迟。由于并行计算能力，深度神经网络（DNN）解码器已成为具有前景的解决方案。本文针对PAC码提出三类DNN解码器：多层感知器（MLP）、卷积神经网络（CNN）和循环神经网络（RNN）。通过广泛仿真评估了这些DNN解码器的性能。数值结果表明，在相似模型参数量下，MLP解码器具有最佳纠错性能。