Secret Key Generation for IRS-Assisted Multi-Antenna Systems: A Machine Learning-Based Approach
Secret Key Generation for IRS-Assisted Multi-Antenna Systems: A Machine Learning-Based Approach
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DOI：
10.1109/tifs.2023.3331588
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发表时间：
2023-04
影响因子：
6.8
通讯作者：
Chen Chen-Chen;Junqing Zhang;Tianyu Lu;M. Sandell;Liquan Chen
中科院分区：
文献类型：
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作者：
Chen Chen-Chen;Junqing Zhang;Tianyu Lu;M. Sandell;Liquan Chen
基于无线信道的物理层密钥生成(PKG)是一种在合法通信节点之间建立安全密钥的轻量级技术。最近，智能反射表面(IRS)被用来提高PKG在密钥速率(SKR)方面的性能，因为它可以重新配置无线传播环境并引入更多的信道随机性。在本文中，我们研究了一种IRS辅助的PKG系统，该系统同时考虑了基站和IRS的信道空间相关性。基于所考虑的系统模型，在考虑相关窃听信道的情况下，得到了SKR的解析表达式。以最大SKR为目标，建立了BS预编码矩阵和IRS相移向量的联合设计问题。针对这种高维非凸优化问题，我们提出了一种结构简单、基于无监督深度神经网络(DNN)的算法。与以往大多数工作采用迭代优化的方法不同，该算法直接得到BS预编码和IRS相移作为DNN的输出。仿真结果表明，基于DNN的算法在SKR方面优于基准算法。
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.