  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.
