@inproceedings{5531e5ca3f9d4a3e84a0a1288599b1bc,
title = "Autoencoding Graph Neural Networks for Scalable Transceiver Design",
abstract = "Autoencoder (AE) techniques have been intensively studied for the optimization of wireless transceivers. However, fixed computational structures of existing AE models lack the flexibility to the lengths of message bits and codewords. This work proposes a versatile AE framework, termed by autoencoding graph neural network (AEGNN), where both encoder and decoder are realized by GNNs. The viability of the proposed AEGNN is demonstrated in various application scenarios.",
keywords = "autoencoder, deep learning, Graph neural network, scalable transceiver design, symbol error rate",
author = "Junbeom Kim and Hoon Lee and Park, \{Seok Hwan\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 ; Conference date: 26-09-2022 Through 29-09-2022",
year = "2022",
doi = "10.1109/VTC2022-Fall57202.2022.10012954",
language = "English",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings",
}