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

Chen, C ORCID: 0000-0002-5161-8973, Zhang, J ORCID: 0000-0002-3502-2926, Lu, T ORCID: 0000-0002-7958-1594, Sandell, M ORCID: 0000-0001-8625-5394 and Chen, L ORCID: 0000-0002-7202-4939 (2024) Secret Key Generation for IRS-Assisted Multi-Antenna Systems: A Machine Learning-Based Approach IEEE Transactions on Information Forensics and Security, 19. pp. 1086-1098. ISSN 1556-6013, 1556-6021

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	4613 Theory Of Computation, 46 Information and Computing Sciences, 4006 Communications Engineering, 40 Engineering, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Bioengineering
Divisions:	Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	06 Nov 2023 08:38
Last Modified:	22 Jan 2026 09:12
DOI:	10.1109/TIFS.2023.3331588
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3176629
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