GNN-Based Power Allocation and User Association in Digital Twin Network for the Terahertz Band-Reference-Cited by-同舟云学术

GNN-Based Power Allocation and User Association in Digital Twin Network for the Terahertz Band

Published:2023-10 Issue:10 Volume:41 Page:3111-3121
ISSN:0733-8716
Container-title:IEEE Journal on Selected Areas in Communications
language:
Short-container-title:IEEE J. Select. Areas Commun.

Author:

Zhang Haijun¹^ORCID,Ma Xu¹^ORCID,Liu Xiangnan¹^ORCID,Li Linpei¹^ORCID,Sun Kai²^ORCID

Affiliation:

1. Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, University of Science and Technology Beijing, Beijing, China

2. College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Xiaomi Fund of Young Scholar

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/49/10297956/10253478.pdf?arnumber=10253478

Reference33 articles.

1. Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

2. Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

3. Beyond 100 Gb/s Optoelectronic Terahertz Communications: Key Technologies and Directions

4. Last Meter Indoor Terahertz Wireless Access: Performance Insights and Implementation Roadmap

5. A GNN-Based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. LLM-Twin: mini-giant model-driven beyond 5G digital twin networking framework with semantic secure communication and computation;Scientific Reports;2024-08-17

2. Meta-Learning for Wireless Communications: A Survey and a Comparison to GNNs;IEEE Open Journal of the Communications Society;2024

3. A Comprehensive Survey on Revolutionizing Connectivity Through Artificial Intelligence-Enabled Digital Twin Network in 6G;IEEE Access;2024

4. Guest Editorial Digital Twins for Mobile Networks—Part I;IEEE Journal on Selected Areas in Communications;2023-10