@inproceedings{1153256c355d4a2db9f7d38bc09d34ab,
title = "Deep Learning-Based Massive MIMO CSI Feedback",
abstract = "Massive multi-input and multi-output technology is a key technology for future 5G wireless communication. The channel feedback problem of massive mimo becomes more and more challenging as the size of the mimo channel matrix becomes larger. A supervised deep learning-based encoder-decoder scheme was proposed to improve recinstruction quality recovery channel state information.Compared with the traditional compression-based sensing algorithm, Residual Attention-Net can still maintain good performance when compression is low.",
keywords = "attention model, compressed sensing, deep learning, massive MIMO, residual network",
author = "Jialing Li and Zihe Gao and Jinxi Qian and Qi Zhang and Xiangjun Xin and Ying Tao and Qinghua Tian and Feng Tian and Dong Chen and Yufei Shen and Guixing Cao",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 18th International Conference on Optical Communications and Networks, ICOCN 2019 ; Conference date: 05-08-2019 Through 08-08-2019",
year = "2019",
month = aug,
doi = "10.1109/ICOCN.2019.8934725",
language = "English",
series = "2019 18th International Conference on Optical Communications and Networks, ICOCN 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 18th International Conference on Optical Communications and Networks, ICOCN 2019",
address = "United States",
}