Chen, Chen, Zhang, Junqing 
ORCID: 0000-0002-3502-2926, Lu, Tianyu, Sandell, Magnus and Chen, Liquan
(2023)
Machine Learning-Based Secret Key Generation for IRS-Assisted Multi-Antenna Systems.
In: ICC 2023 - IEEE International Conference on Communications, 2023-5-28 - 2023-6-1.
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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. Aiming to maximize the SKR, a joint design problem of the BS's precoding matrix and the IRS's 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.
| Item Type: | Conference or Workshop Item (Unspecified) |
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| Additional Information: | (c) 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| Uncontrolled Keywords: | 7 Affordable and Clean Energy |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 20 Mar 2023 09:37 |
| Last Modified: | 15 Mar 2024 14:44 |
| DOI: | 10.1109/icc45041.2023.10279041 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3169156 |
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