Game-theoretic and Learning-aided Physical Layer Security for Multiple Intelligent Eavesdroppers-Reference-Cited by-同舟云学术

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Game-theoretic and Learning-aided Physical Layer Security for Multiple Intelligent Eavesdroppers

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Globecom Workshops (GC Wkshps)
language:
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Author:

Wu Yingzhen¹,Huo Yan¹,Gao Qinghe¹,Wu Yue¹,Li Xuehan¹

Affiliation:

1. Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China

Funder

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10008390/10008474/10008668.pdf?arnumber=10008668

Reference17 articles.

1. Secure Transmission Scheme Based on Fingerprint Positioning in Cell-Free Massive MIMO Systems

2. Impact Analysis of Mobility on Physical Layer Security of mmWave Networks

3. A Deep Reinforcement Learning Based Offloading Game in Edge Computing

4. Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks

5. Artificial-Noise-Aided Transmission in Multi-Antenna Relay Wiretap Channels With Spatially Random Eavesdroppers

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1. Noncooperative game-theoretic model for security enhancement in cloud gaming environments;International Journal of Modeling, Simulation, and Scientific Computing;2024-08-28

2. FlexD: Enhancing Secrecy Performance of Wireless Networks with Dynamic Scheduling;ICC 2024 - IEEE International Conference on Communications;2024-06-09

3. New Game Artificial Intelligence Tools for Virtual Mine on Unreal Engine;Applied Sciences;2023-05-22

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