Decoding of Polar Code by Machine Learning In this paper, we proposed a block neural network (BlockNN) algorithm for polar code. We equally divide the 2n bit polar code into many small subblocks according to the encoding rules of polar code, then put these subblocks into the neural network of the same structure for processing. This decoding algorithm is noniterative and inherently enables a high level of parallelization, while showing a competitive BER(bit error arte) performance. On the aspect of hardware implementation, this decoding structure of the neural network can be multiplexed and the computational complexity does not increase with the code length, only related to the size of the block.

A Novel Decoding Scheme for Polar Code Using Convolutional Neural Network Polar code is the first capacityachieving channel coding scheme which has been selected for the next generation of wireless communication standard(5G). However, the traditional decoding schemes such as CASCL and BP algorithms cannot take into account both BER and latency performance. Inspired by the deep learning technology, we propose a scheme that combine convolutional neural network (CNN) with traditional Belief Propagation (BP) decoding. By exploiting noise correlation for channel decoding under colored noise environment, the proposed scheme can obtain a more accurate estimation of channel noise. We simulate the performance of the proposed scheme under BPSK and 4QAM modulation. For QAM modulation, a scheme that extend the conventional realvalued CNN technique to the complexvalued one is given. The results demonstrate that bit error rates (BER) performance can be improved after removing the estimated noise. Compare to the traditional Polar decoding schemes, the proposed scheme has better tradeoff between BER and latency performance under colored noise environment.