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）码在某些码率情况下可以达到色散近似界。然而，传统PAC译码算法的串行译码特性导致较高的译码延迟。得益于并行计算能力，深度神经网络（DNN）译码器已成为一种有前景的解决方案。本文针对PAC码提出了三种类型的DNN译码器：多层感知机（MLP）、卷积神经网络（CNN）和循环神经网络（RNN）。通过大量仿真评估了这些DNN译码器的性能。数值结果表明，在模型参数数量相近的情况下，MLP译码器具有最佳的纠错性能。