Research on polar Decoding Method Based on Convolutional Neural Network
MengXue Yan, Zhongdong Wu, Xiang Hui Liu · 2021
In order to overcome the problems of high bit error rate, delay and complexity, research on CNN polar decoding algorithm is proposed. This method uses CNN as the decoder, analyzes the difference between the traditional algorithm and the CNN decoder, adjusts the network parameters, obtains the best parameters, makes the decoder optimal, and in the channel coding scheme, the encoding and decoding complexity of polarized codes is low, and it has been strictly proved that they can reach the Shannon limit. However, there are a lot of shortcomings in the existing traditional decoding algorithms, With the maturity of deep learning, the application of deep learning to the field of communication, deep learning has advantage of powerful computing, and neural network after training is static, once only need data through the network, so the deep learning was applied to the polarization code decoding process, can effectively reduce the decoding delay and improve the efficiency of decoding, this paper mainly studies convolution decoder of neural networks in all aspects of performance, can get the conclusion by experiment, with the increase of the number of iterations network decoding ability improved, error performance is getting better and better, and CNN has obvious advantages.