@inproceedings{c7b95573fddd48fbb1c1abbd66d39b54,
title = "Decoding of Polar Code by Machine Learning",
abstract = "In this paper, we proposed a block neural network (BlockNN) algorithm for polar code. We equally divide the 2n bit polar code into many small sub-blocks according to the encoding rules of polar code, then put these sub-blocks into the neural network of the same structure for processing. This decoding algorithm is non-iterative and inherently enables a high level of parallelization, while showing a competitive BER(bit error arte) performance. On the aspect of hardware implementation, this decoding structure of the neural network can be multiplexed and the computational complexity does not increase with the code length, only related to the size of the block.",
keywords = "5G, channel coding, decoder, machine learning, neural network, polar code",
author = "Yi Jian and Rongke Liu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2019 ; Conference date: 05-06-2019 Through 07-06-2019",
year = "2019",
month = jun,
doi = "10.1109/BMSB47279.2019.8971893",
language = "英语",
series = "IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB",
publisher = "IEEE Computer Society",
booktitle = "2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2019",
address = "美国",
}