A RNN Based Decoder for Polar Codes
Yuan Chai, Zuting Chen, Shuai Han, Huan Li · 2023
Polar code is the first type of constructive coding with achievable capacity, which has become the encoding scheme for 5G mobile communication systems and is a cutting-edge direction in channel coding research. However, traditional decoding algorithms for Polar codes still need to be improved in terms of errorcorrection performance and decoding delay. For Polar code short codes, due to the phenomenon of incomplete Polar, the traditional SC decoding algorithm has a problem of poor decoding performance. Algorithms that utilize deep learning for direct or auxiliary operations often yield more accurate computational results with lower computational complexity. Therefore, considering that applying deep learning to the communication field is a mainstream trend in the 6G era, a (16,8) short Polar code decoder based on recurrent neural network (RNN) is proposed. At the same time, a suitable dataset construction method was proposed, which achieved better performance than SC decoding algorithm under low signal-to-noise ratio by adjusting network parameters and training under the appropriate dataset.