GGCNN: An Efficiency-Maximizing Gated Graph Convolutional Neural Network Architecture for Automatic Modulation Identification-Reference-Cited by-同舟云学术

GGCNN: An Efficiency-Maximizing Gated Graph Convolutional Neural Network Architecture for Automatic Modulation Identification

Published:2023-09 Issue:9 Volume:22 Page:6033-6047
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Ghasemzadeh Pejman¹^ORCID,Hempel Michael¹^ORCID,Wang Honggang²^ORCID,Sharif Hamid¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Advanced Telecommunication Engineering Laboratory (TEL), University of Nebraska–Lincoln, Lincoln, NE, USA

2. Department of Electrical and Computer Engineering, Internet of Things and Data Engineering Laboratory, University of Massachusetts at Dartmouth, Dartmouth, MA, USA

Funder

U.S. Federal Railroad Administration

Rural Railroad Safety Center

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7693/10247109/10032243.pdf?arnumber=10032243

Reference43 articles.

1. Analysis of Distribution Test-based and Feature-based Approaches toward Automatic Modulation Classification

2. Few-shot learning with graph neural networks;garcia;arXiv 1711 04043,2017