A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization-Reference-Cited by-同舟云学术

A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization

Published:2023-01 Issue:1 Volume:22 Page:333-347
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Kim Junbeom¹,Lee Hoon²^ORCID,Hong Seung-Eun¹^ORCID,Park Seok-Hwan³^ORCID

Affiliation:

1. Division of Electronic Engineering and the Future Semiconductor Convergence Technology Research Center, Jeonbuk National University, Jeonju, South Korea

2. Department of Information and Communication Engineering, Pukyong National University, Busan, South Korea

3. Future Mobile Communication Research Division, Electronics and Telecommunications Research Institute, Daejeon, South Korea

Funder

Institute of Information and Communications Technology Planning and Evaluation (IITP) Grants by MSIT

(5G Open Intelligence-Defined RAN

National Research Foundation (NRF) of Korea Grants by MOE

Ministry of Science and ICT, South Korea

BK21 FOUR Program by Jeonbuk National University Research Grant

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7693/10008384/09844981.pdf?arnumber=9844981

Reference41 articles.

1. Towards Optimal Power Control via Ensembling Deep Neural Networks

2. Densely Connected Convolutional Networks

3. Fast Beamforming Design via Deep Learning

4. Deep Residual Learning for Image Recognition

5. Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

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