Deep-Reinforcement-Learning-Driven Secrecy Design for Intelligent-Reflecting-Surface-Based 6G-IoT Networks-Reference-Cited by-同舟云学术

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Deep-Reinforcement-Learning-Driven Secrecy Design for Intelligent-Reflecting-Surface-Based 6G-IoT Networks

Published:2023-05-15 Issue:10 Volume:10 Page:8812-8824
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Saleem Rabbia¹^ORCID,Ni Wei¹^ORCID,Ikram Muhammad²^ORCID,Jamalipour Abbas³^ORCID

Affiliation:

1. School of Engineering, College of Engineering and Computer Science, The Australian National University, Canberra, ACT, Australia

2. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia

3. Faculty of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10119650/09999559.pdf?arnumber=9999559

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1. Enhancing physical layer security with reconfigurable intelligent surfaces and friendly jamming: A secrecy analysis;Computer Communications;2024-05

2. Over-the-air federated learning: Status quo, open challenges, and future directions;Fundamental Research;2024-02

3. Future Communication Systems: Toward the Intelligent-based Optimization Approaches;2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM);2023-10-26

4. A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications;Sensors;2023-02-24

5. Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey;IEEE Communications Surveys & Tutorials;2023

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