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

IEEE Vehicular Technology Conference

Boosting Secret Key Generation for IRS-Assisted Symbiotic Radio Communications

Yang Liu, Meng Wang, Jing Xu, Shimin Gong, Dinh Thai Hoang, Dusit Tao Niyato

Published Apr 1, 2021• 16 citations
Publisher

Abstract

Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS’s phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS’s random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS’s phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.

DOI
10.1109/vtc2021-spring51267.2021.9448719

Related papers

Intelligent Reflecting Surface-Assisted Physical Layer Key Generation with Deep Learning in MIMO Systems

Shengjie Liu, Wei Guo, Haoyu He +5 more

Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness.

Sensors•Dec 21, 2022•13 citations

Machine Learning-Based Secret Key Generation for IRS-assisted Multi-antenna Systems

Chen Chen, Junqing Zhang, Tianyu Lu +2 more

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed form expression of SKR is derived analytically. Aiming to maximize the SKR, a joint design problem of the BS precoding matrix and the IRS reflecting coefficient vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN) based algorithm with a simple structure. Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.

arXiv:2301.08179•Jan 19, 2023•0 citations

Secret Key Generation for Intelligent Reflecting Surface Assisted Wireless Communication Networks with Multiple Eavesdroppers

Ya Liu, Kaizhi Huang, Shaochuan Yang +2 more

Physical layer secret key generation (SKG) is a promising supplement for high-level encryption, which leverages the channel reciprocity and the natural randomness of wireless channel. However, practical implementations often exhibit static environment. To solve this problem, this paper proposes a novel secret key generation scheme assisted by an intelligent reflecting surface (IRS) which can smartly reconfigure the radio signal propagation to improve the secret key rate of SKG. Specially, we formulate the minimum achievable secret key capacity in the IRS-assisted multiple-input single-output (MISO) system with multiple eavesdroppers. Then, an optimization framework for designing IRS reflecting coefficients based on the secret key capacity lower bound is proposed. Numerical results show that our optimal IRS-assisted SKG scheme can achieve much higher achievable secret key capacity when compared with two benchmark schemes.

International Conference on Advanced Computing and Endogenous Security•Apr 21, 2022•2 citations

Intelligent Reflecting Surface-Assisted Secret Key Generation In Multi-antenna Network

You Chen, Guyue Li, Cunhua Pan +2 more

Physical-layer key generation (PKG) can generate symmetric keys between two communication ends based on the reciprocal uplink and downlink channels. By smartly reconfiguring the radio signal propagation, intelligent reflecting surface (IRS) is able to improve the secret key rate of PKG. However, existing works involving IRS-assisted PKG are concentrated in single-antenna wireless networks. So this paper investigates the problem of PKG in the IRS-assisted multiple-input single-output (MISO) system, which aims to maximize the secret key rate by optimally designing the IRS passive beamforming. First, we analyze the correlation between channel state information (CSI) of eavesdropper and legitimate ends and derive the expression of the upper bound of secret key rate under passive eavesdropping attack. Then, an optimal algorithm for designing IRS reflecting coefficients based on Semi-Definite Relaxation (SDR) and Taylor expansion is proposed to maximize the secret key rate. Numerical results show that our optimal IRS-assisted PKG scheme can achieve much higher secret key rate when compared with two benchmark schemes.

arXiv:2105.00511•May 2, 2021•4 citations

Intelligent Reflecting Surface-Assisted Wireless Key Generation for Low-Entropy Environments

Paul Staat, Harald Elders-Boll, Markus Heinrichs +3 more

Physical layer key generation is a promising candidate for cryptographic key establishment between two wireless communication parties. It offers information-theoretic security and is an attractive alternative to public-key techniques. Here, the inherent randomness of wireless radio channels is used as a shared entropy source to generate cryptographic key material. However, practical implementations often suffer from static channel conditions which exhibit a limited amount of randomness. In the past, considerable research efforts have been made to address this fundamental limitation. However, current solutions are not generic or require dedicated hardware extensions such as reconfigurable antennas. In this paper, we propose a novel wireless key generation architecture based on randomized channel responses from an intelligent reflecting surface (IRS). Due to its passive nature, a cooperative IRS is well-suited to provide randomness for conventional resource-constrained radios. We conduct the first practical studies to successfully demonstrate IRS-based physical-layer key generation with an OFDM system. In a static environment, using a single subcarrier only, our IRS-assisted prototype system achieves a key generation rate (KGR) of 97.39 bps with 6.5% key disagreement rate (KDR) after quantization, while passing standard randomness tests.

arXiv:2010.06613•IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)•Sep 13, 2021•65 citations

Secret Key Generation for IRS-Assisted Multi-Antenna Systems: A Machine Learning-Based Approach

Chen Chen, Junqing Zhang, Tianyu Lu +2 more

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically considering correlated eavesdropping channels. Aiming to maximize the SKR, a joint design problem of the BS’s precoding matrix and the IRS’s phase shift vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN)-based algorithm with a simple structure. Different from most previous works that adopt iterative optimization to solve the problem, the proposed DNN-based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.

arXiv:2305.00043•IEEE Transactions on Information Forensics and Security•Nov 10, 2023•11 citations
Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS’s phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS’s random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS’s phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.
Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness.
Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed form expression of SKR is derived analytically. Aiming to maximize the SKR, a joint design problem of the BS precoding matrix and the IRS reflecting coefficient vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN) based algorithm with a simple structure. Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.
Physical layer secret key generation (SKG) is a promising supplement for high-level encryption, which leverages the channel reciprocity and the natural randomness of wireless channel. However, practical implementations often exhibit static environment. To solve this problem, this paper proposes a novel secret key generation scheme assisted by an intelligent reflecting surface (IRS) which can smartly reconfigure the radio signal propagation to improve the secret key rate of SKG. Specially, we formulate the minimum achievable secret key capacity in the IRS-assisted multiple-input single-output (MISO) system with multiple eavesdroppers. Then, an optimization framework for designing IRS reflecting coefficients based on the secret key capacity lower bound is proposed. Numerical results show that our optimal IRS-assisted SKG scheme can achieve much higher achievable secret key capacity when compared with two benchmark schemes.
Physical-layer key generation (PKG) can generate symmetric keys between two communication ends based on the reciprocal uplink and downlink channels. By smartly reconfiguring the radio signal propagation, intelligent reflecting surface (IRS) is able to improve the secret key rate of PKG. However, existing works involving IRS-assisted PKG are concentrated in single-antenna wireless networks. So this paper investigates the problem of PKG in the IRS-assisted multiple-input single-output (MISO) system, which aims to maximize the secret key rate by optimally designing the IRS passive beamforming. First, we analyze the correlation between channel state information (CSI) of eavesdropper and legitimate ends and derive the expression of the upper bound of secret key rate under passive eavesdropping attack. Then, an optimal algorithm for designing IRS reflecting coefficients based on Semi-Definite Relaxation (SDR) and Taylor expansion is proposed to maximize the secret key rate. Numerical results show that our optimal IRS-assisted PKG scheme can achieve much higher secret key rate when compared with two benchmark schemes.
Physical layer key generation is a promising candidate for cryptographic key establishment between two wireless communication parties. It offers information-theoretic security and is an attractive alternative to public-key techniques. Here, the inherent randomness of wireless radio channels is used as a shared entropy source to generate cryptographic key material. However, practical implementations often suffer from static channel conditions which exhibit a limited amount of randomness. In the past, considerable research efforts have been made to address this fundamental limitation. However, current solutions are not generic or require dedicated hardware extensions such as reconfigurable antennas. In this paper, we propose a novel wireless key generation architecture based on randomized channel responses from an intelligent reflecting surface (IRS). Due to its passive nature, a cooperative IRS is well-suited to provide randomness for conventional resource-constrained radios. We conduct the first practical studies to successfully demonstrate IRS-based physical-layer key generation with an OFDM system. In a static environment, using a single subcarrier only, our IRS-assisted prototype system achieves a key generation rate (KGR) of 97.39 bps with 6.5% key disagreement rate (KDR) after quantization, while passing standard randomness tests.
Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically considering correlated eavesdropping channels. Aiming to maximize the SKR, a joint design problem of the BS’s precoding matrix and the IRS’s phase shift vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN)-based algorithm with a simple structure. Different from most previous works that adopt iterative optimization to solve the problem, the proposed DNN-based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.