A decoding algorithm for Polar codes based on PD-CNN

Since the traditional decoding algorithms of Polar code are seriously degraded under the correlated noise channel, a decoding algorithm based on the Prior Decision-Convolutional Neural Networks (PD-CNN) is proposed. The CNN is optimized depth by the pre-predicted in order to accurately estimate the channel noise and make the residual noise follow the Gaussian distribution as much as possible, and make the reliable likelihood ratio information be updated according to the residual noise distribution statistics. The decoding performances of different decoding algorithms for different code rate of Polar codes under different noise correlation intensities are analyzed and compared with the decoding algorithm proposed in this paper. The simulation results show that the proposed decoding algorithm can obtain a gain of about 2.5 dB compared with the standard Belief Propagation algorithm at the bit error rate（BER）of 10-5 in the correlated noise channel. In addition,the proposed decoding algorithm has the same performance compared with the Belief Propagation-Convolution Neural Network algorithm at high signal-to-noise ratio（SNR）, but the complexity is lower, and the decoding delay can be reduced by 42%.