@inproceedings{0a56739bc39443ed8eb788c7d67432c7,
title = "A rate-compatible punctured Polar code decoding scheme based on deep learning",
abstract = "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.",
keywords = "Polar codes, deep learning, rare-compatible",
author = "Wanqi Li and Qinghua Tian and Yuqing Zhang and Feng Tian and Zhipei Li and Qi Zhang and Yongjun Wang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 20th International Conference on Optical Communications and Networks, ICOCN 2022 ; Conference date: 12-08-2022 Through 15-08-2022",
year = "2022",
doi = "10.1109/ICOCN55511.2022.9900977",
language = "English",
series = "ICOCN 2022 - 20th International Conference on Optical Communications and Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICOCN 2022 - 20th International Conference on Optical Communications and Networks",
address = "United States",
}