When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges-Reference-Cited by-同舟云学术

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When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Published:2022 Issue: Volume:3 Page:176-208
ISSN:2644-125X
Container-title:IEEE Open Journal of the Communications Society
language:
Short-container-title:IEEE Open J. Commun. Soc.

Author:

Wang Qun¹^ORCID,Sun Haijian²,Hu Rose Qingyang¹^ORCID,Bhuyan Arupjyoti³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Utah State University, Logan, UT, USA

2. Department of Computer Science, University of Wisconsin–Whitewater, Whitewater, WI, USA

3. Wireless Security Institute, Idaho National Laboratory, Idaho Falls, ID, USA

Funder

Idaho National Laboratory

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications

Link

https://ieeexplore.ieee.org/ielam/8782661/9702748/9693972-aam.pdf

Reference163 articles.

1. A Learning-Based QoE-Driven Spectrum Handoff Scheme for Multimedia Transmissions over Cognitive Radio Networks

2. Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach

3. Harvest-or-Transmit Policy for Cognitive Radio Networks: A Learning Theoretic Approach

4. Energy-Efficient Resource Allocation in Cognitive Radio Networks Under Cooperative Multi-Agent Model-Free Reinforcement Learning Schemes

5. Q-Learning Based Multi-Objective Clustering Algorithm for Cognitive Radio Ad Hoc Networks

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4. Discrimination of primary user emulation attack on cognitive radio networks using machine learning based spectrum sensing scheme;Wireless Networks;2024-04-01

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