Autoencoding Graph Neural Networks for Scalable Transceiver Design

被引：0

作者：

Kim, Junbeom ^{[1
]}

Lee, Hoon ^{[2
]}

Park, Seok-Hwan ^{[1
]}

机构：

[1] Jeonbuk Natl Univ, Div Elect Engn, Jeonju 54896, South Korea

[2] Pukyong Natl Univ, Dept Informat & Commun Engn, Busan 48513, South Korea

来源：

2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL) | 2022年

基金：

新加坡国家研究基金会;

关键词：

Graph neural network; autoencoder; deep learning; symbol error rate; scalable transceiver design;

D O I：

10.1109/VTC2022-Fall57202.2022.10012954

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：2

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