@inproceedings{f2a12d71a970475d8136f7c5f1614214,
title = "Quantize-and-Forward Relay System with Autoencoder Using Multiple Antennas",
abstract = "In this paper, we propose a multi-input multi-output (MIMO) relay system with an autoencoder that jointly optimizes the transmitter and receiver applying deep learning. In this communication system, a memory-limited quantize-and-forward (QF) relay assists conventional point-to-point communications. With deep learning, the receiver (destination) does not need to estimate the channel information and avoids the high-complexity maximum-likelihood detection in the MIMO QF relay system, and thus this system can be a good alternative to next-generation communications which require high data rate and low latency.",
keywords = "Amplify-and-forward, autoencoder, deep learning, machine learning, multi-input multi-output, quantize-and-forward, relay",
author = "Juin Shin and Xianglan Jin",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022 ; Conference date: 26-10-2022 Through 28-10-2022",
year = "2022",
doi = "10.1109/ICCE-Asia57006.2022.9954881",
language = "English",
series = "2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022",
}