Deep Learning aided BP-Flip Decoding of Polar Codes

This paper proposes the deep neural network (DNN) based on a belief propagation flip (BPF) decoding algorithm. The conventional BPF decoding does not determine which bits to flip and exhaustively flips the bits of a critical set (CS). This paper uses a DNN to decide which bits to flip. In addition, to reduce the training complexity of DNN, the codeword segmentation and the classes consisting of CS were used. As a result, the proposed BP-DNN-Flip algorithm shows a performance gain of 0.3dB at frame error rate (FER) 10(-4) compared to the conventional BPF decoding algorithm. In addition, it has a lower average time complexity of at least 55% compared to traditional BPF decoding algorithms at a signal-to-noise ratio (SNR) of 1.5dB.