Polar codes decoding algorithm based on convolutional neural network - Rui GUO; Fanchun RAN (Dianxin kexue)
In order to solve the problem that the existing Polar code decoding algorithm based on neural network can only decode short codewords (codewords length N≤64),a new decoding algorithm using convolution neural network for long codewords (N≥512) was put forward.Instead of using a fixed drawn from the data sample proportion as training set and test set,the neural network parameters epoch and batch were used to control the neural network input,and the data explosion problem caused by the long codewords was solved.In addition,the influence of batch and epoch parameters on the decoding performance of convolution neural network was explored and the performance difference of neural network using different activation functions were investigated.Simulation results show that with the traditional SCL (successive cancellation list,L=2) decoding algorithm,convolution neural network in low signal-to-noise ratio on the performance is better than that of SCL (L=2),in high signal-to-noise ratio and SCL (L=2) algorithm of similar performance,and the larger the training data set,the better the decoding performance of neural network.