Multi-Agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks-Reference-Cited by-同舟云学术

Multi-Agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks

Published:2023-03 Issue:3 Volume:22 Page:1900-1914
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Du Xiao¹^ORCID,Wang Ting¹^ORCID,Feng Qiang²,Ye Chenhui²,Tao Tao²^ORCID,Wang Lu³^ORCID,Shi Yuanming⁴^ORCID,Chen Mingsong¹^ORCID

Affiliation:

1. Shanghai Key Laboratory of Trustworthy Computing, MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, China

2. Bell Laboratories, Nokia Shanghai Bell Corporation, Shanghai, China

3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

4. School of Information Science and Technology, ShanghaiTech University, Shanghai, China

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Plan

Dean’s Fund of Engineering Research Center of Software/Hardware Co-Design Technology and Application, Ministry of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7693/10064475/09904510.pdf?arnumber=9904510

Reference52 articles.

1. Artificial neural networks-based machine learning for wireless networks: A tutorial;chen;arXiv 1710 02913,2017

2. ITLinQ+: An improved spectrum sharing mechanism for device-to-device communications

3. Distributed Dynamic Channel Allocation in 6G in-X Subnetworks for Industrial Automation

4. Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

5. Continuous control with deep reinforcement learning;lillicrap;arXiv 1509 02971,2015

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