Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Lecture notes in computer science

POSTER: AI-Based Physical Layer Key Generation Mechanism

Hong Qiang Zhao, Zhuotao Lian, Xinsheng Wang, Enting Guo

Published Oct 10, 2025• 0 citations
Publisher
DOI
10.1007/978-981-95-2961-2_28

Related papers

Poster: Key Generation Scheme Based on Physical Layer

Hong Qiang Zhao, Chunhua Su

Lecture notes in computer science•Jan 1, 2022•0 citations

AI-Based Physical Layer Key Generation in Wireless Communications: Current Advances, Open Challenges, and Future Directions

Jiajun Li, Yishan Yang, Zheng Yan

Physical layer secret key generation (PHYSKG) is a promising technology to provide lightweight and information-theoretically secure key sharing. It has been widely studied in wireless communications and the Internet of Things (IoT). Yet, this technology is nontrivial for large scale wireless networks due to high overhead, insufficient security, and poor adaptability. Recently, artificial intelligence (AI)-based PHYSKG has shown greater performance and higher security with lower overhead than traditional schemes. Although many existing works have been devoted to exploiting AI-based PHYSKG, a scientific review is still missing, but highly anticipated in the literature. In this article, we perform a serious review on AI-based PHYSKG in wireless communications. After a comprehensive introduction to the processing procedure of AI-based PHYSKG, we propose a set of criteria for justifying its performance with regard to key generation efficiency, reliability, security, and generality. Based on these criteria, we review and analyze the state-of-art, focusing on randomness extraction and quantification. On the basis of the review, we further indicate open issues, current challenges, and future research direction in this field.

IEEE Wireless Communications•Jul 1, 2024•11 citations

Min-Entropy Estimation for Physical Layer Key Generation: An Empirical Study

Dongchi Han, Yuan Ma, Tianyu Chen +2 more

Lecture notes in computer science•Jan 1, 2026•0 citations

On the using of discrete wavelet transform for physical layer key generation

Furui Zhan, Nianmin Yao

Ad Hoc Networks•Jun 16, 2017•49 citations

AI-Based Physical Layer Secret Key Exchange in Non-Terrestrial Wireless Communication Networks

Asim Ul Haq, Albena Mihovska, Pavlos I. Lazaridis +3 more

Physical layer security is a promising technology for non-terrestrial wireless networks due to the lack of infrastructure over the low-altitude and high-altitude uncrewed aerial vehicle platforms. Physical layer security can be addressed using key-based and keyless approaches, such as channel adaptation and channel coding. In the key-based approach, the legitimate users generate secret keys by exploiting the unique random channel gains, which can be further used for information encryption and decryption. In non-terrestrial networks, the physical layer exploitation and the matching of generated secret keys on legitimate ends become more complex due to the 3-dimensional channel models, and the high mobility of uncrewed aerial vehicles, resulting in nonzero key. To address this challenge, this paper proposes an artificial intelligence-based key exchange mechanism where the secret keys are generated on the legitimate nodes of the communications links. The transmitter is responsible for training the neural network and transmitting the final synapses to the receiver to reconcile the information. The results are presented in terms of secret key waveforms. The success rate against the number of errors that occur in a single 128-bit key and 256-bit key shows a significant improvement.

Asilomar Conference on Signals, Systems and Computers (ACSSC)•Oct 27, 2024•1 citation

On the using of Rényi’s quadratic entropy for physical layer key generation

Furui Zhan, Zixiang Zhao, Yuhua Chen +1 more

Computer Communications•Feb 15, 2019•12 citations
Physical layer secret key generation (PHYSKG) is a promising technology to provide lightweight and information-theoretically secure key sharing. It has been widely studied in wireless communications and the Internet of Things (IoT). Yet, this technology is nontrivial for large scale wireless networks due to high overhead, insufficient security, and poor adaptability. Recently, artificial intelligence (AI)-based PHYSKG has shown greater performance and higher security with lower overhead than traditional schemes. Although many existing works have been devoted to exploiting AI-based PHYSKG, a scientific review is still missing, but highly anticipated in the literature. In this article, we perform a serious review on AI-based PHYSKG in wireless communications. After a comprehensive introduction to the processing procedure of AI-based PHYSKG, we propose a set of criteria for justifying its performance with regard to key generation efficiency, reliability, security, and generality. Based on these criteria, we review and analyze the state-of-art, focusing on randomness extraction and quantification. On the basis of the review, we further indicate open issues, current challenges, and future research direction in this field.
Physical layer security is a promising technology for non-terrestrial wireless networks due to the lack of infrastructure over the low-altitude and high-altitude uncrewed aerial vehicle platforms. Physical layer security can be addressed using key-based and keyless approaches, such as channel adaptation and channel coding. In the key-based approach, the legitimate users generate secret keys by exploiting the unique random channel gains, which can be further used for information encryption and decryption. In non-terrestrial networks, the physical layer exploitation and the matching of generated secret keys on legitimate ends become more complex due to the 3-dimensional channel models, and the high mobility of uncrewed aerial vehicles, resulting in nonzero key. To address this challenge, this paper proposes an artificial intelligence-based key exchange mechanism where the secret keys are generated on the legitimate nodes of the communications links. The transmitter is responsible for training the neural network and transmitting the final synapses to the receiver to reconcile the information. The results are presented in terms of secret key waveforms. The success rate against the number of errors that occur in a single 128-bit key and 256-bit key shows a significant improvement.