DRL-Driven Digital Twin Function Virtualization for Adaptive Service Response in 6G Networks-Reference-Cited by-同舟云学术

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DRL-Driven Digital Twin Function Virtualization for Adaptive Service Response in 6G Networks

Published:2023-06 Issue:2 Volume:5 Page:125-129
ISSN:2576-3156
Container-title:IEEE Networking Letters
language:
Short-container-title:IEEE Netw. Lett.

Author:

Tao Yihang¹^ORCID,Wu Jun²^ORCID,Lin Xi¹^ORCID,Yang Wu³

Affiliation:

1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

2. Graduate School of Information, Production and Systems, Waseda University, Fukuoka, Japan

3. Information Security Research Center, Harbin Engineering University, Harbin, China

Funder

JSPS KAKENHI

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Economics, Econometrics and Finance

Link

http://xplorestaging.ieee.org/ielx7/8253410/10147268/10107755.pdf?arnumber=10107755

Reference10 articles.

1. Application-Aware Consensus Management for Software-Defined Intelligent Blockchain in IoT

2. Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

3. Intelligent Digital Twin-Based Software-Defined Vehicular Networks

4. Stochastic Digital-Twin Service Demand with Edge Response: An Incentive-Based Congestion Control Approach

5. Adaptive Digital Twin and Multiagent Deep Reinforcement Learning for Vehicular Edge Computing and Networks

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1. O-RAN-Based Digital Twin Function Virtualization for Sustainable IoV Service Response: An Asynchronous Hierarchical Reinforcement Learning Approach;IEEE Transactions on Green Communications and Networking;2024-09

2. Future Directions of Digital Twin Architectures for 6G Communication Networks;Advances in Wireless Technologies and Telecommunication;2024-06-28

3. Digital Twins for Healthcare Using Wearables;Bioengineering;2024-06-13

4. Energy Harvesting Design for Cooperative Reconfigurable Intelligent Surface with Multi-Agent Deep Reinforcement Learning;2024 10th IEEE International Conference on Intelligent Data and Security (IDS);2024-05-10

5. A Survey of PAPR Techniques Based on Machine Learning;Sensors;2024-03-16

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