A rate-compatible punctured Polar code decoding scheme based on deep learning
Wanqi Li, Qinghua Tian, Yuqing Zhang, Feng Tian, Zhipei Li, Qi Zhang, Yongjun Wang · 2022 20th International Conference on Optical Communications and Networks (ICOCN) · 2022
In order to improve transmission reliability and flexible cooperation in optical communication, rate-compatible punctured Polar codes have become a research hotspot. Aiming at the problem that the traditional decoding performance and transmission efficiency is limited, based on deep learning, a rate-compatible punctured Polar code decoding scheme is studied. We use convolutional neural network model as the basic structure of rate-compatible Polar code decoder. The log likelihood ratio values of the received sequence are input into the decoder for training. Simulation results show that the proposed decoder outperforms the traditional punctured Polar code decoder under high signal-to-noise ratio.