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

TL;DR

This paper introduces a machine learning approach to optimize secret key generation in IRS-assisted multi-antenna systems, considering channel correlation, and demonstrates improved key rates over existing methods.

Contribution

It develops a novel DNN-based algorithm for joint optimization of BS precoding and IRS phase shifts in correlated channel environments, enhancing secret key rates.

Findings

The derived SKR expression accounts for channel correlation and eavesdropper channels.

The proposed DNN method outperforms benchmark algorithms in simulations.

The approach effectively maximizes secret key rate in IRS-assisted systems.

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 maximise the SKR, a joint design problem of the BS precoding matrix and the IRS phase shift vector is formulated. To address this high-dimensional non-convex optimisation problem, we propose a novel unsupervised deep neural network (DNN)-based algorithm with a simple structure. Different from most previous works that adopt iterative optimisation 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.