  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.
