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

arXiv:2607.14505 · International Conference on Communications

Denoising-Autoencoder-Assisted Physical Layer Secret Key Generation

Zhengyu Xu, Shun Kojima, Shinya Sugiura

Published May 24, 2026• 0 citations
arXivHTMLPDF

Abstract

In this paper, we propose denoising autoencoder (DAE)-assisted secret key generation (SKG), where channel noise reciprocity imperfections induced due to wireless channel measurements are suppressed, hence significantly enhancing the reliability and efficiency. More specifically, the DAE is capable of capturing the intrinsic structure of input distributions, reconstructing the original data structure, and removing additive noise while preserving the essential structure of signals. In our analysis, it is demonstrated that the proposed SKG scheme exhibits higher performance than the conventional schemes in terms of key disagreement rate (KDR), secret key capacity (SKC), and randomness of the generated keys.

DOI
10.1109/icc59461.2026.11587401

Related papers

Autoencoder-Based Noise Augmentation for Physical Layer Secret Key Generation in IoT Networks

Anas Alashqar, Ehsan Olyaei Torshizi, Raed Mesleh +1 more

Physical Layer Secret Key Generation (PSKG) exploits wireless channel reciprocity to establish cryptographic keys between legitimate users, providing a promising solution for securing IoT networks. However, reciprocity in time-division duplex (TDD) systems is often compromised by hardware imperfections and channel noise, posing significant challenges for reliable key generation. To address these issues, this paper proposes an autoencoder-based noise augmentation (AE-NA) model that operates in an unsupervised manner. Specifically, the proposed model integrates dynamic Gaussian noise augmentation directly into the autoencoder architecture. This approach enhances feature extraction capabilities and improves noise robustness, enabling the network to learn noise-invariant representations effectively from noisy channel estimates. Furthermore, we present a comprehensive PSKG framework that incorporates the proposed AE-NA model. Reported results demonstrate that the AE-NA-based PSKG significantly reduces the key disagreement ratio (KDR) while enhancing key randomness, thereby providing a scalable and effective solution for secure key generation in dynamic IoT environments.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)•Sep 1, 2025•0 citations

Sparse Autoencoder-Driven Key Generation for Enhanced IoT Security

Anas Alashqar, Raed Mesleh, Ehsan Olyaei Torshizi +1 more

Physical-layer secret key generation (PSKG) is a well-known and effective method for boosting wireless security in the Internet of Things (IoT). This technique creates cryptographic keys from correlated wireless channel measurements by taking advantage of the reciprocal nature of uplink and downlink channels. However, in time-division duplex (TDD) systems, issues such as hardware imperfection and noise can disrupt channel reciprocity, making the key generation process more difficult. This paper presents a new PSKG approach that tackles these issues by using deep learning to enhance channel reciprocity, even with imperfect channel state information (CSI). Specifically, a denoising sparse autoencoder (DSAE) is introduced to efficiently learn and extract channel reciprocity features in TDD OFDM systems. Furthermore, this study presents a low-complexity DSAE-based PSKG scheme that utilizes the phase of channel frequency responses (CFR) for key generation. The reported results indicate that the proposed DSAE-based PSKG method notably decreases the key disagreement ratio (KDR) while improving randomness, offering a robust and practical solution for securing wireless communications in IoT environments.

IEEE Globecom Workshops (GC Wkshps)•Dec 8, 2024•4 citations

Improved Channel Information Extraction toward Efficient Secret Key Generation

Yuqin Gan, Xia Lei, Yue Xiao +2 more

Exploiting the reciprocity and randomness of the wireless channel for secret key generation (SKG) is a promising technique, which attracts great interests recently toward physical layer security. Although various methods have been developed for efficient SKG, the key disagreement rate (KDR) performance is still difficult to be ensured when the transmission channel is with severe interference and noise. To alleviate this problem, this paper presents an efficient channel information extraction algorithm based on extracting effective channel paths to reduce the KDR. Simulation results demonstrate that the proposed algorithm provides significant improvement compared with the existing approaches.

IEEE International Conference on Communication Technology (ICCT)•Oct 1, 2019•7 citations

Physical Layer Secret Key Generation Based on Mutual Information-Driven Autoencoder

Dengke Guo, Jun Xiong, Dongtang Ma +2 more

The reciprocity of wireless channels is a prerequisite for physical layer secret key generation (SKG). However, inherent factors, such as noise, asynchronous observations, and hardware impairments, disrupt the ideal reciprocity in the channel state information (CSI) observed by the two legitimate parties. To address this issue, we propose a mutual information-driven autoencoder (MIAE) architecture to extract reciprocal channel features from the non-ideal channel observations of legitimate parties. MIAE is constructed with an AutoEncoder Network (AENet) and a mutual information neural estimator (MINE). Specifically, AENet employs a structure with dual encoders and a shared decoder. The two encoders, integrated with convolutional block attention modules (CBAMs), are designed to focus on reciprocal features within CSI observations and to compensate for temporal variations caused by asynchronous observations. The shared encoder and the correspondingly designed loss function further concentrate the autoencoder’s reciprocity enhancement capability into the encoders. MINE is integrated into the proposed MIAE to estimate the mutual information between the channel features of the two parties. This estimation is then used to formulate a mutual information loss, which guides the encoders to learn channel features that closely match the optimal distribution, thereby boosting the key generation rate. Furthermore, a complete SKG scheme is designed based on the proposed channel feature extractor, MIAE. Simulation results show that our proposed MIAE can extract channel features with strong reciprocity and thus achieve excellent SKG performance based on the extracted features. The generalization performance of the proposed MIAE architecture for communication scenarios of different scales has also been examined.

IEEE Transactions on Wireless Communications•May 1, 2025•1 citation

On Enhancements of Physical Layer Secret Key Generation and Its Application in Wireless Communication Systems

Kang Liu

As an alternative and appealing approach to providing information security in wireless communication systems, secret key generation at physical layer has demonstrated its potential in terms of efficiency and reliability over traditional cryptographic methods. Without the necessity of a management centre for key distribution or reliance on computational complexity, physical layer key generation protocols enable two wireless entities to extract identical and dynamic keys from the randomness of the wireless channels associated with them.\nIn this thesis, the reliability of secret key generation at the physical layer is examined in practical wireless channels with imperfect channel state information (CSI). Theoretical analyses are provided to relate key match rate with channel's signal-to-noise ratio (SNR), degrees of channel reciprocity, and iterations of information reconciliation.\nIn order to increase key match rate of physical layer secret key generation, improved schemes in the steps of channel estimation and sample quantization are proposed respectively. In the channel estimation step, multiple observations of the wireless channels are integrated with a linear processor to provide a synthesized and more accurate estimation of the wireless channel. In the sample quantization step, a magnitude based quantization method with two thresholds is proposed to quantize partial samples, where specific quantization areas are selected to reduce cross-over errors. Significant improvements in key match rate are proven for both schemes in theoretical analysis and numerical simulations. Key match rate can even achieve 100% in both schemes with the assistance of information reconciliation process.\nIn the end, a practical application of physical layer secret key generation is presented, where dynamic keys extracted from the wireless channels are utilized for securing secret data transmission and providing efficient access control.

•Jan 1, 2015•1 citation

A physical layer secret key consistency enhancement scheme using constellation decision information

Zheyuan Deng, Liang Jin, Xiaoyan Hu +3 more

Secret key generation based on a wireless channel (WC-SKG) is a promising solution to address the security issues in wireless communication. However, the consistency of channel estimation between two legal communication nodes in WC-SKG is often poor due to the receiver noise, signal power, etc., leading to a low secret key generation rate (SKGR). Although there are several denoising algorithms such as orthogonal transformation to address this issue, existing schemes overlook the fact that data symbols are also affected by the channel. This results in existing schemes only using the pilot symbols for channel estimation and not fully utilizing the received signal power of the WC-SKG. To address this issue, we propose a consistency enhancement algorithm based on constellation decision information (CEA-CDI), which utilizes both pilot symbols and soft decision information of data symbols to improve SKGR. Monte Carlo simulation and numerical results demonstrate that our proposed scheme can improve performance by approximately 16 dB compared to initial channel estimation.

Security and Safety•Nov 20, 2023•1 citation
In this paper, we propose denoising autoencoder (DAE)-assisted secret key generation (SKG), where channel noise reciprocity imperfections induced due to wireless channel measurements are suppressed, hence significantly enhancing the reliability and efficiency. More specifically, the DAE is capable of capturing the intrinsic structure of input distributions, reconstructing the original data structure, and removing additive noise while preserving the essential structure of signals. In our analysis, it is demonstrated that the proposed SKG scheme exhibits higher performance than the conventional schemes in terms of key disagreement rate (KDR), secret key capacity (SKC), and randomness of the generated keys.
Physical Layer Secret Key Generation (PSKG) exploits wireless channel reciprocity to establish cryptographic keys between legitimate users, providing a promising solution for securing IoT networks. However, reciprocity in time-division duplex (TDD) systems is often compromised by hardware imperfections and channel noise, posing significant challenges for reliable key generation. To address these issues, this paper proposes an autoencoder-based noise augmentation (AE-NA) model that operates in an unsupervised manner. Specifically, the proposed model integrates dynamic Gaussian noise augmentation directly into the autoencoder architecture. This approach enhances feature extraction capabilities and improves noise robustness, enabling the network to learn noise-invariant representations effectively from noisy channel estimates. Furthermore, we present a comprehensive PSKG framework that incorporates the proposed AE-NA model. Reported results demonstrate that the AE-NA-based PSKG significantly reduces the key disagreement ratio (KDR) while enhancing key randomness, thereby providing a scalable and effective solution for secure key generation in dynamic IoT environments.
Physical-layer secret key generation (PSKG) is a well-known and effective method for boosting wireless security in the Internet of Things (IoT). This technique creates cryptographic keys from correlated wireless channel measurements by taking advantage of the reciprocal nature of uplink and downlink channels. However, in time-division duplex (TDD) systems, issues such as hardware imperfection and noise can disrupt channel reciprocity, making the key generation process more difficult. This paper presents a new PSKG approach that tackles these issues by using deep learning to enhance channel reciprocity, even with imperfect channel state information (CSI). Specifically, a denoising sparse autoencoder (DSAE) is introduced to efficiently learn and extract channel reciprocity features in TDD OFDM systems. Furthermore, this study presents a low-complexity DSAE-based PSKG scheme that utilizes the phase of channel frequency responses (CFR) for key generation. The reported results indicate that the proposed DSAE-based PSKG method notably decreases the key disagreement ratio (KDR) while improving randomness, offering a robust and practical solution for securing wireless communications in IoT environments.
Exploiting the reciprocity and randomness of the wireless channel for secret key generation (SKG) is a promising technique, which attracts great interests recently toward physical layer security. Although various methods have been developed for efficient SKG, the key disagreement rate (KDR) performance is still difficult to be ensured when the transmission channel is with severe interference and noise. To alleviate this problem, this paper presents an efficient channel information extraction algorithm based on extracting effective channel paths to reduce the KDR. Simulation results demonstrate that the proposed algorithm provides significant improvement compared with the existing approaches.
The reciprocity of wireless channels is a prerequisite for physical layer secret key generation (SKG). However, inherent factors, such as noise, asynchronous observations, and hardware impairments, disrupt the ideal reciprocity in the channel state information (CSI) observed by the two legitimate parties. To address this issue, we propose a mutual information-driven autoencoder (MIAE) architecture to extract reciprocal channel features from the non-ideal channel observations of legitimate parties. MIAE is constructed with an AutoEncoder Network (AENet) and a mutual information neural estimator (MINE). Specifically, AENet employs a structure with dual encoders and a shared decoder. The two encoders, integrated with convolutional block attention modules (CBAMs), are designed to focus on reciprocal features within CSI observations and to compensate for temporal variations caused by asynchronous observations. The shared encoder and the correspondingly designed loss function further concentrate the autoencoder’s reciprocity enhancement capability into the encoders. MINE is integrated into the proposed MIAE to estimate the mutual information between the channel features of the two parties. This estimation is then used to formulate a mutual information loss, which guides the encoders to learn channel features that closely match the optimal distribution, thereby boosting the key generation rate. Furthermore, a complete SKG scheme is designed based on the proposed channel feature extractor, MIAE. Simulation results show that our proposed MIAE can extract channel features with strong reciprocity and thus achieve excellent SKG performance based on the extracted features. The generalization performance of the proposed MIAE architecture for communication scenarios of different scales has also been examined.
As an alternative and appealing approach to providing information security in wireless communication systems, secret key generation at physical layer has demonstrated its potential in terms of efficiency and reliability over traditional cryptographic methods. Without the necessity of a management centre for key distribution or reliance on computational complexity, physical layer key generation protocols enable two wireless entities to extract identical and dynamic keys from the randomness of the wireless channels associated with them.\nIn this thesis, the reliability of secret key generation at the physical layer is examined in practical wireless channels with imperfect channel state information (CSI). Theoretical analyses are provided to relate key match rate with channel's signal-to-noise ratio (SNR), degrees of channel reciprocity, and iterations of information reconciliation.\nIn order to increase key match rate of physical layer secret key generation, improved schemes in the steps of channel estimation and sample quantization are proposed respectively. In the channel estimation step, multiple observations of the wireless channels are integrated with a linear processor to provide a synthesized and more accurate estimation of the wireless channel. In the sample quantization step, a magnitude based quantization method with two thresholds is proposed to quantize partial samples, where specific quantization areas are selected to reduce cross-over errors. Significant improvements in key match rate are proven for both schemes in theoretical analysis and numerical simulations. Key match rate can even achieve 100% in both schemes with the assistance of information reconciliation process.\nIn the end, a practical application of physical layer secret key generation is presented, where dynamic keys extracted from the wireless channels are utilized for securing secret data transmission and providing efficient access control.
Secret key generation based on a wireless channel (WC-SKG) is a promising solution to address the security issues in wireless communication. However, the consistency of channel estimation between two legal communication nodes in WC-SKG is often poor due to the receiver noise, signal power, etc., leading to a low secret key generation rate (SKGR). Although there are several denoising algorithms such as orthogonal transformation to address this issue, existing schemes overlook the fact that data symbols are also affected by the channel. This results in existing schemes only using the pilot symbols for channel estimation and not fully utilizing the received signal power of the WC-SKG. To address this issue, we propose a consistency enhancement algorithm based on constellation decision information (CEA-CDI), which utilizes both pilot symbols and soft decision information of data symbols to improve SKGR. Monte Carlo simulation and numerical results demonstrate that our proposed scheme can improve performance by approximately 16 dB compared to initial channel estimation.